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  • MPI Ethno. Forsch.  (115)
  • English  (110)
  • Chinese  (6)
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  • 2020-2024  (115)
  • 1990-1994
  • 1980-1984
  • Big data
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  • 1
    Online Resource
    Online Resource
    Foundations of data mesh (2024)
    [Place of publication not identified] : O'Reilly Media, Inc.
    Details
    Language: English
    Pages: 1 online resource (1 video file (3 hr., 9 min.)) , sound, color.
    Edition: [First edition].
    DDC: 658.4/038011
    Keywords: Management information systems ; Business Data processing ; Big data ; Systèmes d'information de gestion ; Gestion ; Informatique ; Données volumineuses ; Instructional films ; Nonfiction films ; Internet videos ; Films de formation ; Films autres que de fiction ; Vidéos sur Internet
    Abstract: An increasing number of organizations are adopting data mesh due to its ability to explain and address the frustrations that many organizations experience as they try to get value from data at scale. Data mesh treats data as a product, considers domains as a primary concern, applies platform thinking to create self-serve data infrastructure, and introduces a federated computational model of data governance. This course will show you how to holistically combine the changes to the technical landscape and the operational model required for data mesh to succeed. You will explore domain ownership, data as a product, how to create a strong developer experience, and federated governance. Once you have completed the course, you will be able to understand the different elements of data mesh in practice and have the confidence to drive data mesh adoption in your company. What you'll learn and how you can apply it: Understand the intent and value behind the foundational elements of data mesh, and have the confidence to participate in a data mesh adoption. This course is for you because: You are or are interested in becoming, involved in a data mesh adoption. You have a stake in successful data use at scale-you're a machine learning practitioner, a data engineer, an engineering manager with responsibility for data or a leader of a technical organization. You are interested in problems at the intersection of business, data and collaboration. Prerequisites Either: Practical experience implementing distributed transactional systems. Practical experience implementing analytical systems. Recommended follow-up: Read Designing Data-Intensive Applications to deepen your technical understanding of data systems at scale. Read Creating a Data-Driven Organisation for a broader look at how to structure an organization to maximize value from data. Read Fundamentals of Data Observability to learn about how to automate data quality checks. Read Enterprise Data Catalog for a deep dive into the discovery part of data management.
    Note: Online resource; title from title details screen (O'Reilly, viewed February 20, 2024)
    URL: lizenzpflichtig
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    Online Resource
    MPI Ethno. Forsch.
  • 2
    Online Resource
    Online Resource
    Data superstream : Data lakes and warehouses 2024 (2024)
    [Place of publication not identified] : O'Reilly Media, Inc.
    Details
    Language: English
    Pages: 1 online resource (1 video file (3 hr., 31 min.)) , sound, color.
    Edition: [First edition].
    DDC: 658.4/03802855745
    Keywords: Data warehousing ; Big data ; Entrepôts de données (Informatique) ; Données volumineuses ; Instructional films ; Nonfiction films ; Internet videos ; Films de formation ; Films autres que de fiction ; Vidéos sur Internet
    Abstract: Sponsored by Dremio Storing, processing, and moving data efficiently and cost-effectively in the cloud is a must for working with today's enormous datasets. These expert-led sessions deliver insight into how to increase the scalability, speed, and availability of your data and equip you with a set of best practices for utilizing your data warehouse, data lake, or data lakehouse. What you'll learn and how you can apply it Get an overview of the latest technologies for storing and managing your data Learn best practices for standardizing lakehouse data modeling and pipelining Find out how to streamline data lake implementation Explore how to securely develop and implement ML models on a cloud data warehouse Discover the benefits of data contracts in the modern data stack Understand how to enhance lakehouse infrastructure for multimodal AI This Superstream recording is for you because... You're a data or software engineer or solution architect who's interested in learning about the latest trends in storing, processing, and managing data. You want to improve the scalability, speed, and availability of your data. You want to better understand the systems that you already use and learn how to take full advantage of their capabilities. Recommended follow-up: Read Deciphering Data Architectures (early release book) Read Practical Lakehouse Architecture (early release book) Please note that slides or supplemental materials are not available for download from this recording. Resources are only provided at the time of the live event.
    Note: Online resource; title from title details screen (O'Reilly, viewed February 20, 2024)
    URL: lizenzpflichtig
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    Online Resource
    MPI Ethno. Forsch.
  • 3
    Online Resource
    Online Resource
    What's new in software architecture : Data mesh and the AI revolution with Zhamak Dehghani (2024)
    [Place of publication not identified] : O'Reilly Media, Inc.
    Details
    Language: English
    Pages: 1 online resource (1 video file (58 min.)) , sound, color.
    Edition: [First edition].
    DDC: 005.3
    Keywords: Software architecture ; Database management ; Big data ; Architecture logicielle ; Bases de données ; Gestion ; Données volumineuses ; Instructional films ; Nonfiction films ; Internet videos ; Films de formation ; Films autres que de fiction ; Vidéos sur Internet
    Abstract: Join Neal Ford and Zhamak Dehghani for a discussion about the challenges of creating, sharing, and finding data and making your data work for you. With the rise of generative AI, companies need to manage large quantities of data and are looking at distributed data systems and data mesh to solve this challenge. The problem consists in getting from their existing data storage to a distributed data system that works for all stakeholders. As the originator of the concept of data mesh, and because of her ongoing work with scaling decentralized data, Zhamak is well positioned to provide updates and advice on this more holistic approach to data management. What's New in Software Architecture gives you a chance to explore trending topics in software architecture and to ask Neal and Zhamak Dehghani anything you want about data mesh, how to begin making the move to decentralized data, and their own career journeys. We will spend a few minutes covering the trends that are influencing software architecture and then tell you what you need to know to stay ahead of the curve. What you'll learn and how you can apply it Understand what a data mesh is and how it differs from other methods of storing and sharing data Learn the key data characteristics you need to be a data-led organization Explore the tools that can help you make the move to data mesh This live course is for you because... You want to hear from Neal Ford and Zhamak Dehghani about software architecture and data mesh. Recommended Follow-Up: Read Building an Event-Driven Data Mesh (book) Read Data Mesh (book) Please note that slides or supplemental materials are not available for download from this recording. Resources are only provided at the time of the live event.
    Note: Online resource; title from title details screen (O'Reilly, viewed February 20, 2024)
    URL: lizenzpflichtig
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    Online Resource
    MPI Ethno. Forsch.
  • 4
    Online Resource
    Online Resource
    Python Polars : the definitive guide : a blazingly fast, multi-threaded, elegant API for data loading, manipulation, and processing (2024)
    Sebastopol, CA : O'Reilly Media, Inc.
    Details
    Language: English
    Pages: 1 online resource (400 pages) , illustrations
    Edition: First edition.
    DDC: 005.7
    Keywords: Big data ; Data mining ; Python (Computer program language) ; Données volumineuses ; Exploration de données (Informatique) ; Python (Langage de programmation)
    Abstract: Want to speed up your data analysis and work with larger-than-memory datasets? Python Polars offers a blazingly fast, multithreaded, and elegant API for data loading, manipulation, and processing. With this hands-on guide, you'll walk through every aspect of Polars and learn how to tackle practical use cases using real-world datasets. Jeroen Janssens and Thijs Nieuwdorp from Xomnia in Amsterdam show you how this superfast DataFrame library is perfect for efficient data wrangling, ETL pipelines, and so much more. This book helps you quickly learn the syntax and understand Polars' underlying concepts. You don't need to have experience with pandas or Spark, but if you do, this book will help you make a smooth transition.
    URL: lizenzpflichtig
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    Online Resource
    MPI Ethno. Forsch.
  • 5
    Online Resource
    Online Resource
    Use tweaks and optimization techniques for data generation (2024)
    [Sebastopol, California] : O'Reilly Media, Inc.
    Details
    ISBN: 9781098167417 , 1098167414
    Language: English
    Pages: 1 online resource (5 pages)
    Edition: [First edition].
    Series Statement: Shortcuts
    DDC: 006.3/2
    Keywords: Neural networks (Computer science) ; Big data ; Réseaux neuronaux (Informatique) ; Données volumineuses
    Abstract: These shortcuts delve into generative AI, where algorithms and models create synthetic data, detect anomalies, and help confirm statistical properties. They explore how generative AI is reshaping risk management, fraud detection, and data simulation, and they offer a unique synthesis of theory and practical applications.
    URL: lizenzpflichtig
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    Online Resource
    MPI Ethno. Forsch.
  • 6
    Online Resource
    Online Resource
    Architecting a modern data warehouse for large enterprises : build multi-cloud modern distributed data warehouses with Azure and AWS (2024)
    New York, NY : Apress
    Details
    ISBN: 9798868800290
    Language: English
    Pages: 1 online resource (378 pages) , illustrations
    Edition: [First edition].
    Parallel Title: Erscheint auch als
    Keywords: Amazon Web Services (Firm) ; Data warehousing ; Microsoft Azure (Computing platform) ; Big data ; Business enterprises Data processing ; Entrepôts de données (Informatique) ; Microsoft Azure (Plateforme informatique) ; Données volumineuses ; Entreprises ; Informatique
    Abstract: Design and architect new generation cloud-based data warehouses using Azure and AWS. This book provides an in-depth understanding of how to build modern cloud-native data warehouses, as well as their history and evolution. The book starts by covering foundational data warehouse concepts, and introduces modern features such as distributed processing, big data storage, data streaming, and processing data on the cloud. You will gain an understanding of the synergy, relevance, and usage data warehousing standard practices in the modern world of distributed data processing. The authors walk you through the essential concepts of Data Mesh, Data Lake, Lakehouse, and Delta Lake. And they demonstrate the services and offerings available on Azure and AWS that deal with data orchestration, data democratization, data governance, data security, and business intelligence. After completing this book, you will be ready to design and architect enterprise-grade, cloud-based modern data warehouses using industry best practices and guidelines.
    Note: Includes index
    URL: lizenzpflichtig
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    Online Resource
    MPI Ethno. Forsch.
  • 7
    Online Resource
    Online Resource
    It's all analytics : Part III: The applications of AI, analytics, and data science (2024)
    New York, NY : Productivity Press
    Details
    ISBN: 9780429343971 , 0429343973 , 9781000928433 , 1000928438 , 1000928446 , 9781000928440
    Language: English
    Pages: 1 online resource (xx, 240 pages)
    Keywords: Artificial intelligence ; Decision making ; Visual analytics ; Big data ; Intelligence artificielle ; Prise de décision ; Analyse visuelle ; Données volumineuses ; artificial intelligence ; decision making
    Abstract: Professionals are challenged each day by a changing landscape of technology and terminology. In recent history, especially the last 25 years, there has been an explosion of terms and methods born that automate and improve decision-making and operations. One term, called "analytics," is an overarching description of a compilation of methodologies. But artificial intelligence (AI), statistics, decision science, and optimization, which have been around for decades, have resurged. Also, things like business intelligence, online analytical processing (OLAP) and many, many more have been born or reborn. How is someone to make sense of all this methodology, terminology? Extending on the foundations introduced in the first book, this book illustrates how professionals in healthcare, business, and government are applying these disciplines, methods, and technologies. The goal of this book is to get leaders and practitioners to start thinking about how they may deploy techniques outside their function or industry into their domain. Application of modern technology into new areas is one of the fastest, most effective ways to improve results. By providing a rich set of examples, this book fosters creativity in the application and use of AI and analytics in innovative ways.
    URL: lizenzpflichtig
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  • 8
    Online Resource
    Online Resource
    Scalable AI and design patterns : design, develop, and deploy scalable AI solutions (2024)
    Berkeley, CA : Apress L. P.
    Details
    ISBN: 9798868801587
    Language: English
    Pages: 1 online resource (xvii, 256 pages) , illustrations.
    Parallel Title: Erscheint auch als
    DDC: 006.3
    Keywords: Computational intelligence ; Artificial intelligence ; Data mining ; Big data ; Intelligence informatique ; Intelligence artificielle ; Exploration de données (Informatique) ; artificial intelligence
    Abstract: Understand and apply the design patterns outlined in this book to design, develop, and deploy scalable AI solutions that meet your organization's needs and drive innovation in the era of intelligent automation. This book begins with an overview of scalable AI systems and the importance of design patterns in creating robust intelligent solutions. It covers fundamental concepts and techniques for achieving scalability in AI systems, including data engineering practices and strategies. The book also addresses scalable algorithms, models, infrastructure, and architecture considerations. Additionally, it discusses deployment, productionization, real-time and streaming data, edge computing, governance, and ethics in scalable AI. Real-world case studies and best practices are presented, along with insights into future trends and emerging technologies. The book focuses on scalable AI and design patterns, providing an understanding of the challenges involved in developing AI systems that can handle large amounts of data, complex algorithms, and real-time processing. By exploring scalability, you will be empowered to design and implement AI solutions that can adapt to changing data requirements. What You Will Learn Develop scalable AI systems that can handle large volumes of data, complex algorithms, and real-time processing Know the significance of design patterns in creating robust intelligent solutions Understand scalable algorithms and models to handle extensive data and computing requirements and build scalable AI systems Be aware of the ethical implications of scalable AI systems Who This Book Is For AI practitioners, data scientists, and software engineers with intermediate-level AI knowledge and experience.
    Note: Description based upon print version of record
    URL: lizenzpflichtig
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    MPI Ethno. Forsch.
  • 9
    Online Resource
    Online Resource
    Big data, emerging technologies and intelligence : national security disrupted (2024)
    Abingdon, Oxon : Routledge
    Details
    ISBN: 9781003389651 , 1003389651 , 9781003836247 , 1003836240 , 9781003836223 , 1003836224
    Language: English
    Pages: 1 online resource (202 pages) , illustrations.
    Series Statement: Studies in intelligence
    Parallel Title: Erscheint auch als
    Keywords: Intelligence service ; National security ; Cyber intelligence (Computer security) ; Electronic surveillance ; Big data ; Service des renseignements ; Australie ; Surveillance des menaces informatiques ; Australie ; Surveillance électronique ; Australie ; Données volumineuses ; POLITICAL SCIENCE / Political Freedom & Security / Intelligence ; POLITICAL SCIENCE / Political Freedom & Security / International Security ; POLITICAL SCIENCE / Political Freedom & Security / Law Enforcement ; Big data ; Cyber intelligence (Computer security) ; Electronic surveillance ; Intelligence service ; National security ; Australia
    Abstract: "This book sets out the big data landscape, comprising data abundance, digital connectivity and ubiquitous technology, and shows how it is impacting national security. The main themes are that big data is transforming intelligence production as well as changing the national security environment broadly, including what is considered a part of national security as well as the relationships agencies have with the people. The book highlights the impact of big data on intelligence production and national security from the perspective of Australian national security leaders and practitioners, and the research is based on empirical data collection, with insights from nearly 50 participants from within Australia's National Intelligence Community. It argues that big data is transforming intelligence and national security and shows that the impacts of big data on the knowledge, activities and organisation of intelligence agencies is challenging some foundational intelligence principles, including the distinction between foreign and domestic intelligence collection. Furthermore, the book argues that big data has created emerging threats to national security; for example, it enables invasive targeting and surveillance, drives information warfare as well as social and political interference, and challenges the existing models of harm assessment used in national security. The book maps broad areas of change for intelligence agencies in the national security context and what they mean for intelligence communities, and explores how intelligence agencies look out to the rest of society, considering specific impacts relating to privacy, ethics and trust. This book will be of much interest to students of intelligence studies, technology studies, national security and International Relations"--
    Note: Includes bibliographical references and index. - Description based on online resource; title from digital title page (viewed on February 20, 2024)
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  • 10
    Online Resource
    Online Resource
    Fundamentals of observability with OpenTelemetry (2024)
    [Place of publication not identified] : O'Reilly Media, Inc.
    Details
    Language: English
    Pages: 1 online resource (1 video file (2 hr., 56 min.)) , sound, color.
    Edition: [First edition].
    DDC: 005.7
    Keywords: Electronic data processing ; Software engineering ; Big data ; Python (Computer program language) ; Computer programming ; Cloud computing ; Génie logiciel ; Python (Langage de programmation) ; Programmation (Informatique) ; computer programming ; Instructional films ; Nonfiction films ; Internet videos ; Films de formation ; Films autres que de fiction ; Vidéos sur Internet
    Abstract: The systems of today are exponentially more complex than the systems of 15, or even 10 years ago. There are way more moving parts and interactions to keep track of, sometimes leading to systems behaving in very unpredictable ways. In the past, Software Engineers and Site Reliability Engineers (SREs) could rely on logging and monitoring to make sense of their systems. This is no longer the case. The good news is that Observability can help. In this course, you will learn about how Observability can help SREs and Software Engineers make sense of what's going on in their systems. You will also learn about OpenTelemetry: what it is, how it supports Observability goals, how OpenTelemetry instrumentation works, and how the OpenTelemetry Collector and OpenTelemetry Operator further enhance OpenTelemetry's capabilities. You will put OpenTelemetry theory into practice with hands-on exercises which include instrumenting a Python application with OpenTelemetry, configuring the OpenTelemetry Collector, and deploying and configuring the OpenTelemetry Kubernetes Operator. Finally, you will learn what pitfalls to avoid when setting up an Observability practice, to ensure that you and your teams are positioned for success, and explore some advanced Observability use cases supported by OpenTelemetry. What you'll learn and how you can apply it Understand what Observability is, and why it is an important practice for SREs and software engineers Understand how OpenTelemetry helps to achieve Observability, and understand the basic building blocks required to instrument an application Understand the value of the OpenTelemetry Collector, and how to configure and deploy it Understand the value of the OpenTelemetry Operator, and how to configure and deploy it Quickly see OpenTelemetry in action in a complex ecosystem by running the OpenTelemetry Demo App Use OpenTelemetry to instrument a simple Python application and send traces to an Observability back-end via the OpenTelemetry Collector Understand what pitfalls to avoid in order to run a successful Observability practice Understand additional ways in which OpenTelemetry can help achieve Observability This course is for you because... You're a Site Reliability Engineer looking to improve the reliability of your systems. You're a Software Engineer looking to improve the debuggability of your code. Prerequisites: Familiarity with Linux Working knowledge of Python programming Docker fundamentals Git fundamentals Kubernetes fundamentals, including deploying applications to Kubernetes.
    Note: Online resource; title from title details screen (O'Reilly, viewed April 2, 2024)
    URL: lizenzpflichtig
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  • 11
    Online Resource
    Online Resource
    Cognitive analytics and reinforcement learning : theories, techniques and applications (2024)
    Hoboken, NJ : John Wiley & Sons, Inc. | Hoboken, NJ : Scrivener Publishing LLC
    Details
    ISBN: 9781394214068 , 1394214065 , 9781394214051 , 1394214057 , 9781394214037
    Language: English
    Pages: 1 online resource
    Parallel Title: Erscheint auch als
    DDC: 006.3
    Keywords: Soft computing ; Big data ; Informatique douce
    Abstract: COGNITIVE ANALYTICS AND REINFORCEMENT LEARNING The combination of cognitive analytics and reinforcement learning is a transformational force in the field of modern technological breakthroughs, reshaping the decision-making, problem-solving, and innovation landscape; this book offers an examination of the profound overlap between these two fields and illuminates its significant consequences for business, academia, and research. Cognitive analytics and reinforcement learning are pivotal branches of artificial intelligence. They have garnered increased attention in the research field and industry domain on how humans perceive, interpret, and respond to information. Cognitive science allows us to understand data, mimic human cognitive processes, and make informed decisions to identify patterns and adapt to dynamic situations. The process enhances the capabilities of various applications. Readers will uncover the latest advancements in AI and machine learning, gaining valuable insights into how these technologies are revolutionizing various industries, including transforming healthcare by enabling smarter diagnosis and treatment decisions, enhancing the efficiency of smart cities through dynamic decision control, optimizing debt collection strategies, predicting optimal moves in complex scenarios like chess, and much more. With a focus on bridging the gap between theory and practice, this book serves as an invaluable resource for researchers and industry professionals seeking to leverage cognitive analytics and reinforcement learning to drive innovation and solve complex problems. The book's real strength lies in bridging the gap between theoretical knowledge and practical implementation. It offers a rich tapestry of use cases and examples. Whether you are a student looking to gain a deeper understanding of these cutting-edge technologies, an AI practitioner seeking innovative solutions for your projects, or an industry leader interested in the strategic applications of AI, this book offers a treasure trove of insights and knowledge to help you navigate the complex and exciting world of cognitive analytics and reinforcement learning. Audience The book caters to a diverse audience that spans academic researchers, AI practitioners, data scientists, industry leaders, tech enthusiasts, and educators who associate with artificial intelligence, data analytics, and cognitive sciences.
    Note: Description based on online resource; title from digital title page (viewed on April 17, 2024)
    URL: lizenzpflichtig
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  • 12
    Online Resource
    Online Resource
    Convergence of IoT, Blockchain, and Computational Intelligence in Smart Cities (2024)
    Abingdon, England : CRC Press
    Details
    ISBN: 9781000962291 , 1000962296
    Language: English
    Pages: 1 online resource (296 pages) , illustrations.
    Edition: First edition.
    Series Statement: Computational Intelligence Techniques Series
    DDC: 004
    Keywords: Big data ; Cloud computing ; Internet of things ; Données volumineuses ; Infonuagique ; Internet des objets ; Big data ; Cloud computing ; Internet of things
    Abstract: This edited book presents an insight for modelling, procuring, and building the smart city plan using IoT and a security framework using blockchain technology. The applications of Li-Fi and 5G in smart cities are included along with their implementation, challenges, and advantages.
    Note: Includes bibliographical references and index. - Description based on print version record
    URL: lizenzpflichtig
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  • 13
    Online Resource
    Online Resource
    Engineering Data Mesh in Azure Cloud : Implement Data Mesh Using Microsoft Azure's Cloud Adoption Framework (2024)
    Birmingham : Packt Publishing, Limited
    Details
    ISBN: 9781805128946 , 1805128949
    Language: English
    Pages: 1 online resource (314 p.)
    Edition: 1st edition.
    DDC: 658.4/038011
    Keywords: Management information systems ; Microsoft Azure (Computing platform) ; Cloud computing ; Business Data processing ; Big data ; Systèmes d'information de gestion ; Microsoft Azure (Plateforme informatique) ; Gestion ; Informatique
    Abstract: Overcome data mesh adoption challenges using the cloud-scale analytics framework and make your data analytics landscape agile and efficient by using standard architecture patterns for diverse analytical workloads Key Features Delve into core data mesh concepts and apply them to real-world situations Safely reassess and redesign your framework for seamless data mesh integration Conquer practical challenges, from domain organization to building data contracts Purchase of the print or Kindle book includes a free PDF eBook Book Description Decentralizing data and centralizing governance are practical, scalable, and modern approaches to data analytics. However, implementing a data mesh can feel like changing the engine of a moving car. Most organizations struggle to start and get caught up in the concept of data domains, spending months trying to organize domains. This is where Engineering Data Mesh in Azure Cloud can help. The book starts by assessing your existing framework before helping you architect a practical design. As you progress, you'll focus on the Microsoft Cloud Adoption Framework for Azure and the cloud-scale analytics framework, which will help you quickly set up a landing zone for your data mesh in the cloud. The book also resolves common challenges related to the adoption and implementation of a data mesh faced by real customers. It touches on the concepts of data contracts and helps you build practical data contracts that work for your organization. The last part of the book covers some common architecture patterns used for modern analytics frameworks such as artificial intelligence (AI). By the end of this book, you'll be able to transform existing analytics frameworks into a streamlined data mesh using Microsoft Azure, thereby navigating challenges and implementing advanced architecture patterns for modern analytics workloads. What you will learn Build a strategy to implement a data mesh in Azure Cloud Plan your data mesh journey to build a collaborative analytics platform Address challenges in designing, building, and managing data contracts Get to grips with monitoring and governing a data mesh Understand how to build a self-service portal for analytics Design and implement a secure data mesh architecture Resolve practical challenges related to data mesh adoption Who this book is for This book is for chief data officers and data architects of large and medium-size organizations who are struggling to maintain silos of data and analytics projects. Data architects and data engineers looking to understand data mesh and how it can help their organizations democratize data and analytics will also benefit from this book. Prior knowledge of managing centralized analytical systems, as well as experience with building data lakes, data warehouses, data pipelines, data integrations, and transformations is needed to get the most out of this book.
    Note: Description based upon print version of record. - Parameterize the pipeline
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  • 14
    Online Resource
    Online Resource
    Survey of data science (2024)
    [Birmingham, United Kingdom] : Packt Publishing
    Details
    ISBN: 9781836201892 , 1836201893
    Language: English
    Pages: 1 online resource (1 video file (8 hr., 43 min.)) , sound, color.
    Edition: [First edition].
    DDC: 005.7
    Keywords: Big data ; Data mining ; Exploration de données (Informatique) ; Instructional films ; Nonfiction films ; Internet videos ; Films de formation ; Films autres que de fiction ; Vidéos sur Internet
    Abstract: Our "Survey of Data Science" course provides a deep dive into the world of data analysis and modeling, equipping learners with the skills to transform raw data into insightful decisions. Starting with an introduction to data scientists' roles and activities, the course progresses through data exploration and analysis. You will learn about the importance of data hygiene, statistical foundations, and the power of visualization to communicate data-driven insights effectively. As the course unfolds, it covers complex topics such as handling unstructured data, building associative rules, decision trees, and regression models, and delves into sophisticated areas like neural networks and natural language processing. The Lambda architecture section will illuminate real-time and batch data processing, essential for handling big data scenarios in professional settings. The course not only provides technical skills but also emphasizes practical application, preparing participants to apply their knowledge in real-world situations. This journey will equip you with the expertise to navigate the challenges of modern data science, making you an asset in various industry roles.
    Note: Online resource; title from title details screen (O'Reilly, viewed May 7, 2024)
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  • 15
    Online Resource
    Online Resource
    Data engineering with Google Cloud platform : a guide to leveling up as a data engineer by building a scalable data platform with Google Cloud (2024)
    Birmingham, UK : Packt Publishing Ltd.
    Details
    ISBN: 1835085369 , 9781835085363 , 9781835080115
    Language: English
    Pages: 1 online resource (476 pages) , illustrations
    Edition: Second edition.
    DDC: 005.7
    Keywords: Big data ; Cloud computing
    Abstract: The second edition of Data Engineering with Google Cloud builds upon the success of the first edition by offering enhanced clarity and depth to data professionals navigating the intricate landscape of data engineering. Beyond its foundational lessons, this new edition delves into the essential realm of data governance within Google Cloud, providing you invaluable insights into managing and optimizing data resources effectively. Furthermore, this book helps you stay ahead of the curve by guiding you through the latest technological advancements in the Google Cloud ecosystem. You'll cover essential aspects, from exploring Cloud Composer 2 to the evolution of Airflow 2.5. Additionally, you'll explore how to work with cutting-edge tools like Dataform, DLP, Dataplex, Dataproc Serverless, and Datastream to perform data governance on datasets. By the end of this book, you'll be equipped to navigate the ever-evolving world of data engineering on Google Cloud, from foundational principles to cutting-edge practices.
    Note: Includes bibliographical references and index
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  • 16
    Online Resource
    Online Resource
    Data platforms : Spark to Snowflake (2024)
    [Place of publication not identified] : Pragmatic AI Solutions
    Details
    Language: English
    Pages: 1 online resource (1 video file (55 min.)) , sound, color.
    Edition: [First edition].
    Series Statement: Pragmatic AI labs course
    DDC: 005.7
    Keywords: Big data ; Electronic data processing ; Instructional films ; Nonfiction films ; Internet videos ; Films de formation ; Films autres que de fiction ; Vidéos sur Internet
    Abstract: Big Data Processing with Hadoop, Spark, Snowflake and Databricks. Learn to process big data using popular platforms like Hadoop, Spark, Snowflake and Databricks through live coding examples Learn from O'Reilly author Kennedy Behrman. This video series covers key concepts and tools for big data processing and storage. It introduces platforms like Hadoop, Spark, Snowflake and Databricks, discussing their architectures and use cases. Through live coding demonstrations in Python and SQL, you'll learn to work with these technologies hands-on.
    Note: Online resource; title from title details screen (O'Reilly, viewed May 7, 2024)
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  • 17
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    Online Resource
    Data science : the hard parts : techniques for excelling at data science (2024)
    [Sebastopol, California] : O'Reilly Media, Inc.
    Details
    ISBN: 9781098146443 , 1098146441
    Language: English
    Pages: 1 online resource
    Edition: First edition.
    Parallel Title: Erscheint auch als
    DDC: 004
    Keywords: Electronic data processing ; Big data ; Database management ; Data mining ; Données volumineuses ; Bases de données ; Gestion ; Exploration de données (Informatique)
    Abstract: "This practical guide provides a collection of techniques and best practices that are generally overlooked in most data engineering and data science pedagogy. A common misconception is that great data scientists are experts in the ""big themes"" of the discipline--machine learning and programming. But most of the time, these tools can only take us so far. In practice, the smaller tools and skills really separate a great data scientist from a not-so-great one. Taken as a whole, the lessons in this book make the difference between an average data scientist candidate and a qualified data scientist working in the field. Author Daniel Vaughan has collected, extended, and used these skills to create value and train data scientists from different companies and industries. With this book, you will: Understand how data science creates value Deliver compelling narratives to sell your data science project Build a business case using unit economics principles Create new features for a ML model using storytelling Learn how to decompose KPIs Perform growth decompositions to find root causes for changes in a metric Daniel Vaughan is head of data at Clip, the leading paytech company in Mexico. He's the author of Analytical Skills for AI and Data Science (O'Reilly)."
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  • 18
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    Data analytics & visualization all-in-one for dummies (2024)
    Hoboken, NJ : John Wiley & Sons, Inc.
    Details
    ISBN: 9781394244119 , 1394244118 , 9781394244102 , 139424410X , 9781394244096
    Language: English
    Pages: 1 online resource
    Series Statement: For dummies
    Parallel Title: Erscheint auch als
    DDC: 005.7
    Keywords: Big data ; Data mining ; Information visualization ; Computer programming ; Exploration de données (Informatique) ; Visualisation de l'information ; Programmation (Informatique) ; computer programming
    Abstract: Data Analytics & Visualization All-in-One For Dummies collects the essential information on mining, organizing, and communicating data, all in one place. Clocking in at around 850 pages, this tome of a reference delivers eight books in one, so you can build a solid foundation of knowledge in data wrangling. Data analytics professionals are highly sought after these days, and this book will put you on the path to becoming one. You’ll learn all about sources of data like data lakes, and you’ll discover how to extract data using tools like Microsoft Power BI, organize the data in Microsoft Excel, and visually present the data in a way that makes sense using a Tableau. You’ll even get an intro to the Python, R, and SQL coding needed to take your data skills to a new level. With this Dummies guide, you’ll be well on your way to becoming a priceless data jockey.
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  • 19
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    Data governance with AWS : balancing data access and control by working backwards from your business initiatives (2024)
    Sebastopol, CA : O'Reilly Media, Inc.
    Details
    Language: English
    Pages: 1 online resource (52 pages)
    Edition: First edition.
    DDC: 004.67/82
    Keywords: Amazon Web Services (Firm) ; Data protection ; Cloud computing ; Big data
    Abstract: How can you ensure that your data is in optimal condition to support your specific business initiatives and operations? With this report from AWS, C-suite executives, including CDOs, CAOs, CISOs, and CSOs, will gain insights on how a targeted approach to data governance can enhance data curation, discovery, protection, and sharing capabilities. Our goal is to empower you to curate your data at scale and share it--without compromising compliance and security measures. You'll understand the significance of data governance and how it fosters rapid innovation by optimizing your data resources. And you'll learn how protecting and securely sharing your data with control and clarity will help deliver better business intelligence insights. Data engineers, data architects, and data analysts will also get practical guidance on specific areas such as data integration and metadata cataloging. With this report, you'll explore: Why data governance is so challenging How working backward from business initiatives can help The three pillars of good data governance and the capabilities they require The technical support needed to deploy a good data governance strategy What AWS data governance tools are available, and how they might fit into your strategy About the authors: Ina Felsheim is senior technology leader at AWS with a relentless customer focus. Kevin Lewis is principal program manager for data analytics at AWS Professional Services. Jason Berkowitz is responsible for global data analytics at AWS Professional Services. Joseph D. Stec writes books and articles related to data, AI, and world history.
    Note: Includes bibliographical references
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  • 20
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    Online Resource
    Sociological Foundations of Computational Social Science (2024)
    Singapore : Springer Nature Singapore | Cham : Springer International Publishing AG
    Details
    ISBN: 9789819994328 , 9819994322
    Language: English
    Pages: 1 Online-Ressource (IX, 124 Seiten) , 7 illus.
    Edition: 1st ed. 2024
    Series Statement: Translational Systems Sciences 40
    Parallel Title: Erscheint auch als Sociological Foundations of Computational Social Science
    DDC: 301.01
    Keywords: Sociology ; Statistics  ; Social sciences Philosophy ; Big data ; Sociological Theory ; Statistics in Business, Management, Economics, Finance, Insurance ; Social Theory ; Big Data
    DOI: 10.1007/978-981-99-9432-8
    URL: https://doi.org/10.1007/978-981-99-9432-8
    URL: Inhaltsverzeichnis
    URL: Ausführliche Beschreibung
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  • 21
    Online Resource
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    Data for all (2023)
    Shelter Island, New York : Manning Publications
    Details
    ISBN: 9781633438774 , 1633438775
    Language: English
    Pages: 1 online resource (xxvi, 163 pages) , illustrations
    Parallel Title: Erscheint auch als
    DDC: 658.05
    Keywords: Business Data processing ; Data protection ; Big data
    Abstract: The data you generate every day is the lifeblood of many large companies—and they make billions of dollars using it. In Data for All, bestselling author John K. Thompson outlines how this one-sided data economy is about to undergo a dramatic change. Thompson pulls back the curtain to reveal the true nature of data ownership, and how you can turn your data from a revenue stream for companies into a financial asset for your benefit.
    Note: Includes bibliographical references and index. - Description based on print version record
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  • 22
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    Online Resource
    Data superstream : Data engineering lifecycle (2023)
    [Place of publication not identified] : O'Reilly Media, Inc.
    Details
    Language: English
    Pages: 1 online resource (1 video file (3 hr., 37 min.)) , sound, color.
    Edition: [First edition].
    DDC: 004.67/82
    Keywords: Database management ; Big data ; Data mining ; Instructional films ; Nonfiction films ; Internet videos
    Abstract: Sponsored by Redpanda Millions (if not billions) of touch points from customers, systems, and processes enter the average business's data stream every day. Farther down that stream, analysts, data scientists, and ML engineers take that data and use it to develop hypotheses, identify insights, feed learning models, and so much more. The job of the data engineer is to manage this lifecycle from initial generation through storage to ingestion, transformation, and finally serving the data, using tools like AWS, Azure, Google Cloud, Spark, Kafka, SQL, and many more. It's extremely important and no small feat. That's why data engineering is one of the fastest growing jobs--and why data engineers are employed by many of the most recognizable tech companies in the world, including IBM, Amazon, Microsoft, Apple, Google, and Facebook. Join experienced industry experts to learn how the data engineering lifecycle fits into the overall data lifecycle, explore the technologies you'll need to conquer along the path from generation to service, and better understand how to meet the needs of analysts, scientists, and ML engineers as well as the business stakeholders and customers driving decisions. What you'll learn and how you can apply it Discover how the data engineering lifecycle allows data professionals to design and build a robust architecture Standardize the process of ML model deployment and monitoring with MLOps Learn essential data preprocessing techniques crucial for harnessing the potential of LLMs This live course is for you because... You're a data engineer, ML engineer, or data scientist. You want to effectively approach the data lifecycle from ingestion to labeling to solving problems with machine learning. You want to learn more about prompt engineering and management to tame the inherent unpredictability of AI-generated outputs. Recommended follow-up: Read Fundamentals of Data Engineering (book) Read Designing Machine Learning Systems (book) Read Machine Learning Design Patterns (book).
    Note: Online resource; title from title details screen (O'Reilly, viewed October 10, 2023)
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  • 23
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    Data engineering with AWS : acquire the skills to design and build AWS-based data transformation pipelines like a pro (2023)
    Birmingham, UK : Packt Publishing Ltd.
    Details
    ISBN: 9781804613139 , 1804613134 , 9781804614426
    Language: English
    Pages: 1 online resource (634 pages) , illustrations
    Edition: Second edition.
    Series Statement: Expert insight
    Parallel Title: Erscheint auch als
    DDC: 004.67/82
    Keywords: Amazon Web Services (Firm) ; Cloud computing ; Big data ; Infonuagique ; Données volumineuses
    Abstract: This book, authored by a seasoned Senior Data Architect with 25 years of experience, aims to help you achieve proficiency in using the AWS ecosystem for data engineering. This revised edition provides updates in every chapter to cover the latest AWS services and features, takes a refreshed look at data governance, and includes a brand-new section on building modern data platforms which covers; implementing a data mesh approach, open-table formats (such as Apache Iceberg), and using DataOps for automation and observability. You'll begin by reviewing the key concepts and essential AWS tools in a data engineer's toolkit and getting acquainted with modern data management approaches. You'll then architect a data pipeline, review raw data sources, transform the data, and learn how that transformed data is used by various data consumers. You'll learn how to ensure strong data governance, and about populating data marts and data warehouses along with how a data lakehouse fits into the picture. After that, you'll be introduced to AWS tools for analyzing data, including those for ad-hoc SQL queries and creating visualizations. Then, you'll explore how the power of machine learning and artificial intelligence can be used to draw new insights from data. In the final chapters, you'll discover transactional data lakes, data meshes, and how to build a cutting-edge data platform on AWS. By the end of this AWS book, you'll be able to execute data engineering tasks and implement a data pipeline on AWS like a pro!
    Note: Includes bibliographical references and index
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  • 24
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    DEVELOPING KAGGLE NOTEBOOKS : paving your way to becoming a Kaggle notebooks grandmaster (2023)
    Birmingham, UK : Packt Publishing Ltd.
    Details
    ISBN: 9781805125716 , 1805125710 , 9781805128519
    Language: English
    Pages: 1 online resource
    Parallel Title: Erscheint auch als
    DDC: 006.3/1
    Keywords: Kaggle (Electronic resource) ; Machine learning ; Big data ; Apprentissage automatique ; Données volumineuses
    Abstract: Printed in Color Develop an array of effective strategies and blueprints to approach any new data analysis on the Kaggle platform and create Notebooks with substance, style and impact Leverage the power of Generative AI with Kaggle Models Purchase of the print or Kindle book includes a free PDF eBook Key Features Master the basics of data ingestion, cleaning, exploration, and prepare to build baseline models Work robustly with any type, modality, and size of data, be it tabular, text, image, video, or sound Improve the style and readability of your Notebooks, making them more impactful and compelling Book Description Developing Kaggle Notebooks introduces you to data analysis, with a focus on using Kaggle Notebooks to simultaneously achieve mastery in this fi eld and rise to the top of the Kaggle Notebooks tier. The book is structured as a sevenstep data analysis journey, exploring the features available in Kaggle Notebooks alongside various data analysis techniques. For each topic, we provide one or more notebooks, developing reusable analysis components through Kaggle's Utility Scripts feature, introduced progressively, initially as part of a notebook, and later extracted for use across future notebooks to enhance code reusability on Kaggle. It aims to make the notebooks' code more structured, easy to maintain, and readable. Although the focus of this book is on data analytics, some examples will guide you in preparing a complete machine learning pipeline using Kaggle Notebooks. Starting from initial data ingestion and data quality assessment, you'll move on to preliminary data analysis, advanced data exploration, feature qualifi cation to build a model baseline, and feature engineering. You'll also delve into hyperparameter tuning to iteratively refi ne your model and prepare for submission in Kaggle competitions. Additionally, the book touches on developing notebooks that leverage the power of generative AI using Kaggle Models. What you will learn Approach a dataset or competition to perform data analysis via a notebook Learn data ingestion and address issues arising with the ingested data Structure your code using reusable components Analyze in depth both small and large datasets of various types Distinguish yourself from the crowd with the content of your analysis Enhance your notebook style with a color scheme and other visual effects Captivate your audience with data and compelling storytelling techniques Who this book is for This book is suitable for a wide audience with a keen interest in data science and machine learning, looking to use Kaggle Notebooks to improve their skills and rise in the Kaggle Notebooks ranks. This book caters to: Beginners on Kaggle from any background Seasoned contributors who want to build various skills like ingestion, preparation, exploration, and visualization Expert contributors who want to learn from the Grandmasters to rise into the upper Kaggle rankings Professionals who already use Kaggle for learning and competing.
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  • 25
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    Data mesh (2023)
    [Place of publication not identified] : Ascent Audio
    Details
    ISBN: 9781663721198 , 166372119X
    Language: English
    Pages: 1 online resource (1 audio file (14 hr., 1 min.))
    Edition: [First edition].
    DDC: 005.7
    Keywords: Big data ; Database management ; Business enterprises Data processing ; Information technology Management ; Big data ; Business enterprises ; Data processing ; Database management ; Information technology ; Management ; Audiobooks ; Audiobooks
    Abstract: We're at an inflection point in data, where our data management solutions no longer match the complexity of organizations, the proliferation of data sources, and the scope of our aspirations to get value from data with AI and analytics. In this practical book, Zhamak Dehghani introduces data mesh, a decentralized sociotechnical paradigm drawn from modern distributed architecture that provides a new approach to sourcing, sharing, accessing, and managing analytical data at scale. Dehghani guides practitioners, architects, technical leaders, and decision makers on their journey from traditional big data architecture to a distributed and multidimensional approach to analytical data management. Data mesh treats data as a product, considers domains as a primary concern, applies platform thinking to create self-serve data infrastructure, and introduces a federated computational model of data governance. - Get a complete introduction to data mesh principles and its constituents - Design a data mesh architecture - Guide a data mesh strategy and execution - Navigate organizational design to a decentralized data ownership model - Move beyond traditional data warehouses and lakes to a distributed data mesh.
    Note: Online resource; title from title details screen (O'Reilly, viewed March 28, 2023)
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  • 26
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    Online Resource
    IBM Storage Fusion Backup and Restore for IBM Cloud Pak for Data (2023)
    [Poughkeepsie, New York] : IBM Redbooks
    Details
    ISBN: 9780738461151 , 0738461156
    Language: English
    Pages: 1 online resource (76 pages) , illustrations
    Edition: [First edition].
    DDC: 004.67/82
    Keywords: Cloud computing ; Big data ; Business Data processing ; Information technology Management
    Abstract: IBM Cloud Pak® for Data can be protected with IBM Spectrum Fusion™. This IBM Redpaper publication covers backing up IBM Cloud Pak for Data with a non-disruptive (online) backup and then restoring to an alternate cluster. During an online backup, normal runtime operations in the Cloud Pak for Data cluster continue while the backup completes. The backup process includes creating policies and automating backups in IBM Spectrum Fusion, then protecting Cloud Pak for Data, protecting IBM Spectrum Fusion namespace and the IBM Spectrum® Protect Plus (SPP) catalog. Backup and restore is supported from IBM Storage Fusion HCI to IBM Spectrum Fusion software as well as from IBM Storage Fusion Software to IBM Storage Fusion HCI.
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  • 27
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    O'Reilly Book Club : Joe Reis and Matt Housley on fundamentals of data engineering (2023)
    [Place of publication not identified] : O'Reilly Media, Inc.
    Details
    Language: English
    Pages: 1 online resource (1 video file (56 min.)) , sound, color.
    Edition: [First edition].
    DDC: 004.6782
    Keywords: Database management ; Big data ; Instructional films ; Nonfiction films ; Internet videos
    Abstract: Join us for this edition of O'Reilly Book Club with Joe Reis and Matt Housley, authors of Fundamentals of Data Engineering, to discover the stages of the data engineering lifecycle and learn to identify the various underlying components of the field, including security, data management, and orchestration. Hear from authors, learn tricks of the trade, and listen to stories. What you'll learn and how you can apply it Receive a concise overview of the data engineering landscape Learn how to evaluate the best technologies available through the framework of the data engineering lifecycle Understand data governance and security across the data engineering lifecycle This live event is for you because... You want to go beyond the words on the page and ask your own questions. You're a data engineer who wants to learn about the latest developments in the field. You're a current software engineer or data scientist who wants to transition into data engineering, or who may be doing some data engineering work in your current role. You're a data team manager who wants to understand how to better optimize your team and contribute positively to your organization. Recommended follow-up: Read Fundamentals of Data Engineering (book) Follow Data Engineering Resource Center (expert playlist).
    Note: Online resource; title from title details screen (O'Reilly, viewed October 11, 2023)
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  • 28
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    97 things every data engineer should know (2023)
    [Place of publication not identified] : Ascent Audio
    Details
    ISBN: 9781663732330 , 1663732337
    Language: English
    Pages: 1 online resource (1 sound file (5 hr., 41 min.))
    Edition: [First edition].
    DDC: 005.7
    Keywords: Big data ; Data sets ; Data collection platforms ; Databases ; Electronic data processing ; Audiobooks
    Abstract: Take advantage of today's sky-high demand for data engineers. With this in-depth book, current and aspiring engineers will learn powerful real-world best practices for managing data big and small. Contributors from notable companies including Twitter, Google, Stitch Fix, Microsoft, Capital One, and LinkedIn share their experiences and lessons learned for overcoming a variety of specific and often nagging challenges. Edited by Tobias Macey, host of the popular Data Engineering Podcast, this book presents ninety-seven concise and useful tips for cleaning, prepping, wrangling, storing, processing, and ingesting data. Data engineers, data architects, data team managers, data scientists, machine learning engineers, and software engineers will greatly benefit from the wisdom and experience of their peers. Topics include: - The Importance of Data Lineage-Julien Le Dem - Data Security for Data Engineers-Katharine Jarmul - The Two Types of Data Engineering and Data Engineers-Jesse Anderson - Six Dimensions for Picking an Analytical Data Warehouse-Gleb Mezhanskiy - The End of ETL as We Know It-Paul Singman.
    Note: Online resource; title from title details screen (O'Reilly, viewed November 15, 2023)
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  • 29
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    Data Ingestion with Python cookbook : A Practical Guide to Ingesting, Monitoring, and Identifying Errors in the Data Ingestion Process (2023)
    Birmingham : Packt Publishing, Limited
    Details
    ISBN: 9781837633098 , 1837633096
    Language: English
    Pages: 1 online resource (414 p.)
    Edition: 1st edition.
    DDC: 005.74
    Keywords: Data mining ; Big data ; Electronic data processing ; Python (Computer program language)
    Abstract: Deploy your data ingestion pipeline, orchestrate, and monitor efficiently to prevent loss of data and quality Purchase of the print or Kindle book includes a free PDF eBook Key Features Harness best practices to create a Python and PySpark data ingestion pipeline Seamlessly automate and orchestrate your data pipelines using Apache Airflow Build a monitoring framework by integrating the concept of data observability into your pipelines Book Description Data Ingestion with Python Cookbook offers a practical approach to designing and implementing data ingestion pipelines. It presents real-world examples with the most widely recognized open source tools on the market to answer commonly asked questions and overcome challenges. You'll be introduced to designing and working with or without data schemas, as well as creating monitored pipelines with Airflow and data observability principles, all while following industry best practices. The book also addresses challenges associated with reading different data sources and data formats. As you progress through the book, you'll gain a broader understanding of error logging best practices, troubleshooting techniques, data orchestration, monitoring, and storing logs for further consultation. By the end of the book, you'll have a fully automated set that enables you to start ingesting and monitoring your data pipeline effortlessly, facilitating seamless integration with subsequent stages of the ETL process. What you will learn Implement data observability using monitoring tools Automate your data ingestion pipeline Read analytical and partitioned data, whether schema or non-schema based Debug and prevent data loss through efficient data monitoring and logging Establish data access policies using a data governance framework Construct a data orchestration framework to improve data quality Who this book is for This book is for data engineers and data enthusiasts seeking a comprehensive understanding of the data ingestion process using popular tools in the open source community. For more advanced learners, this book takes on the theoretical pillars of data governance while providing practical examples of real-world scenarios commonly encountered by data engineers.
    Note: Description based upon print version of record
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  • 30
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    Fundamentals of data engineering (2023)
    [Place of publication not identified] : Ascent Audio
    Details
    ISBN: 9781663732354 , 1663732353
    Language: English
    Pages: 1 online resource (1 sound file (17 hr., 30 min.))
    Edition: [First edition].
    DDC: 004.67/82
    Keywords: Database management ; Big data ; Data mining ; Audiobooks
    Abstract: Data engineering has grown rapidly in the past decade, leaving many software engineers, data scientists, and analysts looking for a comprehensive view of this practice. With this practical book, you'll learn how to plan and build systems to serve the needs of your organization and customers by evaluating the best technologies available through the framework of the data engineering lifecycle. Authors Joe Reis and Matt Housley walk you through the data engineering lifecycle and show you how to stitch together a variety of cloud technologies to serve the needs of downstream data consumers. You'll understand how to apply the concepts of data generation, ingestion, orchestration, transformation, storage, and governance that are critical in any data environment regardless of the underlying technology. This book will help you: get a concise overview of the entire data engineering landscape; assess data engineering problems using an end-to-end framework of best practices; cut through marketing hype when choosing data technologies, architecture, and processes; use the data engineering lifecycle to design and build a robust architecture; and incorporate data governance and security across the data engineering lifecycle.
    Note: Online resource; title from title details screen (O'Reilly, viewed November 15, 2023)
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  • 31
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    Data mesh in action (2023)
    Shelter Island, NY : Manning Publications Co.
    Details
    ISBN: 9781633439979 , 1633439976
    Language: English
    Pages: 1 online resource (300 pages) , illustrations
    Parallel Title: Erscheint auch als
    DDC: 658.4/038011
    Keywords: Management information systems ; Business Data processing ; Big data
    Abstract: Revolutionize the way your organization approaches data with a data mesh! This new decentralized architecture outpaces monolithic lakes and warehouses and can work for a company of any size. Data Mesh in Action reveals how this groundbreaking architecture looks for both startups and large enterprises. You won’t need any new technology—this book shows you how to start implementing a data mesh with flexible processes and organizational change. You’ll explore both an extended case study and real-world examples. As you go, you’ll be expertly guided through discussions around Socio-Technical Architecture and Domain-Driven Design with the goal of building a sleek data-as-a-product system. Plus, dozens of workshop techniques for both in-person and remote meetings help you onboard colleagues and drive a successful transition.
    Note: Description based on print version record
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  • 32
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    Data fabric and data mesh approaches with AI : a guide to AI-based data cataloging, governance, integration, orchestration, and consumption (2023)
    New York, NY : Apress
    Details
    ISBN: 9781484292532 , 1484292537
    Language: English
    Pages: 1 online resource (440 pages) , illustrations
    Edition: [First edition].
    DDC: 005.7
    Keywords: Big data ; Artificial intelligence
    Abstract: Understand modern data fabric and data mesh concepts using AI-based self-service data discovery and delivery capabilities, a range of intelligent data integration styles, and automated unified data governance—all designed to deliver "data as a product" within hybrid cloud landscapes. This book teaches you how to successfully deploy state-of-the-art data mesh solutions and gain a comprehensive overview on how a data fabric architecture uses artificial intelligence (AI) and machine learning (ML) for automated metadata management and self-service data discovery and consumption. You will learn how data fabric and data mesh relate to other concepts such as data DataOps, MLOps, AIDevOps, and more. Many examples are included to demonstrate how to modernize the consumption of data to enable a shopping-for-data (data as a product) experience. By the end of this book, you will understand the data fabric concept and architecture as it relates to themes such as automated unified data governance and compliance, enterprise information architecture, AI and hybrid cloud landscapes, and intelligent cataloging and metadata management.
    Note: Includes bibliographical references and index
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  • 33
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    Machine intelligence, big data analytics, and IoT in image processing : practical applications (2023)
    Hoboken, NJ : Scrivener Publishing
    Details
    ISBN: 9781119865513 , 1119865514 , 9781119865506 , 1119865506 , 9781119865490 , 1119865492 , 9781119865049
    Language: English
    Pages: 1 online resource.
    Series Statement: Advances in intelligent and scientific computing
    Parallel Title: Erscheint auch als
    DDC: 006.3/1
    Keywords: Machine learning ; Big data ; Internet of things ; Big data ; Internet of things ; Machine learning
    Abstract: MACHINE INTELLIGENCE, BIG DATA ANALYTICS, AND IoT IN IMAGE PROCESSING Discusses both theoretical and practical aspects of how to harness advanced technologies to develop practical applications such as drone-based surveillance, smart transportation, healthcare, farming solutions, and robotics used in automation. The concepts of machine intelligence, big data analytics, and the Internet of Things (IoT) continue to improve our lives through various cutting-edge applications such as disease detection in real-time, crop yield prediction, smart parking, and so forth. The transformative effects of these technologies are life-changing because they play an important role in demystifying smart healthcare, plant pathology, and smart city/village planning, design and development. This book presents a cross-disciplinary perspective on the practical applications of machine intelligence, big data analytics, and IoT by compiling cutting-edge research and insights from researchers, academicians, and practitioners worldwide. It identifies and discusses various advanced technologies, such as artificial intelligence, machine learning, IoT, image processing, network security, cloud computing, and sensors, to provide effective solutions to the lifestyle challenges faced by humankind. Machine Intelligence, Big Data Analytics, and IoT in Image Processing is a significant addition to the body of knowledge on practical applications emerging from machine intelligence, big data analytics, and IoT. The chapters deal with specific areas of applications of these technologies. This deliberate choice of covering a diversity of fields was to emphasize the applications of these technologies in almost every contemporary aspect of real life to assist working in different sectors by understanding and exploiting the strategic opportunities offered by these technologies. Audience The book will be of interest to a range of researchers and scientists in artificial intelligence who work on practical applications using machine learning, big data analytics, natural language processing, pattern recognition, and IoT by analyzing images. Software developers, industry specialists, and policymakers in medicine, agriculture, smart cities development, transportation, etc. will find this book exceedingly useful.
    Note: Includes bibliographical references and index. - Description based on online resource; title from digital title page (viewed on February 21, 2023)
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  • 34
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    Data science with semantic technologies : Deployment and exploration (2023)
    Boca Raton, FL : CRC Press
    Details
    ISBN: 9781003310792 , 1003310796 , 9781000881271 , 100088127X , 1000881296 , 9781000881295
    Language: English
    Pages: 1 online resource (x, 236 pages)
    Edition: First edition.
    Parallel Title: Erscheint auch als
    Keywords: Big data ; Semantic computing ; Artificial intelligence ; Information science ; Information technology ; TECHNOLOGY / Operations Research ; TECHNOLOGY / Manufacturing ; Artificial intelligence ; Big data ; Information science ; Information technology ; Semantic computing
    Abstract: Gone are the days when data was interlinked with related data by humans and human interpretation was required. Data is no longer just data. It is now considered a Thing or Entity or Concept with meaning, so that a machine not only understands the concept but also extrapolates the way humans do. Data Science with Semantic Technologies: Deployment and Exploration, the second volume of a two-volume handbook set, provides a roadmap for the deployment of semantic technologies in the field of data science and enables the user to create intelligence through these technologies by exploring the opportunities and eradicating the challenges in the current and future time frame. In addition, this book offers the answer to various questions like: What makes a technology semantic as opposed to other approaches to data science? What is knowledge data science? How does knowledge data science relate to other fields? This book explores the optimal use of these technologies to provide the highest benefit to the user under one comprehensive source and title. As there is no dedicated book available in the market on this topic at this time, this book becomes a unique resource for scholars, researchers, data scientists, professionals, and practitioners. This volume can serve as an important guide toward applications of data science with semantic technologies for the upcoming generation.
    Note: Includes bibliographical references and index. - Description based on online resource; title from digital title page (viewed on August 09, 2023)
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  • 35
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    Online Resource
    Introduction to the fundamentals of data engineering (2023)
    [Place of publication not identified] : O'Reilly Media, Inc.
    Details
    Language: English
    Pages: 1 online resource (1 video file (2 hr., 27 min.)) , sound, color.
    Edition: [First edition].
    DDC: 004.67/82
    Keywords: Database management ; Big data ; Data mining ; Instructional films ; Nonfiction films ; Internet videos
    Abstract: This course introduces learners to the fundamentals of data engineering, and is closely related to material in the book Fundamentals of Data Engineering by Joe Reis and Matt Housley. The course is dedicated to teaching data engineering ideas and principles. The goal of this course is to expose students to core concepts and practices to introduce them to the data engineering career path. Further data engineering training can lead to a career in the discipline or allow students to apply data engineering concepts in their current role. The course covers the stages of the data engineering lifecycle (generation, ingestion, storage, transformation, serving) and the major undercurrents that cut across all stages (security, data management, orchestration, etc.) Learners will gain an understanding of the history of data engineering, current trends, and career prospects. What you'll learn and how you can apply it Understand the stages of the data engineering lifecycle Be able to identify the various underlying components of the field, including security, data management, and orchestration This course is for you because... You're a data engineer who wants to gain a broader understanding of data engineering. You may be familiar with specific tools or technologies, but you are looking for a more holistic, vendor agnostic understanding of data engineering practices. You're a software engineer or data scientist who wants to transition into data engineering, or who may even be doing some data engineering work in your current role. You're a data team manager who wants to understand how to better optimize their team and contribute positively to the organization as a whole. Prerequisites: Beginner understanding of the field of data Beginner to mid-level knowledge of cloud systems and databases.
    Note: Online resource; title from title details screen (O'Reilly, viewed Decenber 5, 2023)
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  • 36
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    Online Resource
    Kafka troubleshooting in production : stabilizing Kafka clusters in the cloud and on-premises (2023)
    New York : Apress
    Details
    ISBN: 9781484294901 , 1484294904
    Language: English
    Pages: 1 online resource (xx, 216 pages) , illustrations (chiefly color)
    Parallel Title: Erscheint auch als
    Keywords: Kafka (Electronic resource) ; Big data ; Cloud computing ; Données volumineuses ; Infonuagique ; Big data ; Cloud computing
    Abstract: This book provides Kafka administrators, site reliability engineers, and DataOps and DevOps practitioners with a list of real production issues that can occur in Kafka clusters and how to solve them. The production issues covered are assembled into a comprehensive troubleshooting guide for those engineers who are responsible for the stability and performance of Kafka clusters in production, whether those clusters are deployed in the cloud or on-premises. This book teaches you how to detect and troubleshoot the issues, and eventually how to prevent them. Kafka stability is hard to achieve, especially in high throughput environments, and the purpose of this book is not only to make troubleshooting easier, but also to prevent production issues from occurring in the first place. The guidance in this book is drawn from the author's years of experience in helping clients and internal customers diagnose and resolve knotty production problems and stabilize their Kafka environments. The book is organized into recipe-style troubleshooting checklists that field engineers can easily follow when under pressure to fix an unstable cluster. This is the book you will want by your side when the stakes are high, and your job is on the line. You will: Monitor and resolve production issues in your Kafka clusters Provision Kafka clusters with the lowest costs and still handle the required loads Perform root cause analyses of issues affecting your Kafka clusters Know the ways in which your Kafka cluster can affect its consumers and producers Prevent or minimize data loss and delays in data streaming Forestall production issues through an understanding of common failure points Create checklists for troubleshooting your Kafka clusters when problems occur.
    Note: Includes index. - Print version record
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  • 37
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    AI and Data Literacy : Empowering Citizens of Data Science (2023)
    Birmingham : Packt Publishing, Limited
    Details
    ISBN: 1835087949 , 9781835087947 , 9781835083505
    Language: English
    Pages: 1 online resource (239 p.)
    DDC: 006.3
    Keywords: Artificial intelligence ; Big data ; Information literacy ; Intelligence artificielle ; Données volumineuses ; Culture de l'information ; artificial intelligence
    Abstract: Learn the key skills and capabilities that empower Citizens of Data Science to not only survive but thrive in an AI-dominated world. Purchase of the print or Kindle book includes a free PDF eBook Key Features Prepare for a future dominated by AI and big data Enhance your AI and data literacy with real-world examples Learn how to leverage AI and data to address current and future challenges Book Description AI is undoubtedly a game-changing tool with immense potential to improve human life. This book aims to empower you as a Citizen of Data Science, covering the privacy, ethics, and theoretical concepts you'll need to exploit to thrive amid the current and future developments in the AI landscape. We'll explore AI's inner workings, user intent, and the critical role of the AI utility function while also briefly touching on statistics and prediction to build decision models that leverage AI and data for highly informed, more accurate, and less risky decisions. Additionally, we'll discuss how organizations of all sizes can leverage AI and data to engineer or create value. We'll establish why economies of learning are more powerful than the economies of scale in a digital-centric world. Ethics and personal/organizational empowerment in the context of AI will also be addressed. Lastly, we'll delve into ChatGPT and the role of Large Language Models (LLMs), preparing you for the growing importance of Generative AI. By the end of the book, you'll have a deeper understanding of AI and how best to leverage it and thrive alongside it. What you will learn Get to know the fundamentals of data literacy, privacy, and analytics Find out what makes AI tick and the role of the AI utility function Make informed decisions using prominent decision-making frameworks Understand relevant statistics and probability concepts Create new sources of value by leveraging and applying AI and data Apply ethical parameters to AI development with real-world examples Find out how to get the most out of ChatGPT and its peers Who this book is for This book is designed to benefit everyone from students to established business leaders and professionals who want to learn how to leverage data and analytics to accelerate their AI and Data literacy.
    Note: Description based upon print version of record. - Inflection points
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  • 38
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    Implementing data mesh (2023)
    Sebastopol, CA : O'Reilly Media, Inc.
    Details
    Language: English
    Pages: 1 online resource (300 pages) , illustrations
    Edition: [First edition].
    DDC: 658.4/038011
    Keywords: Management information systems ; Business Data processing ; Big data ; Systèmes d'information de gestion ; Gestion ; Informatique ; Données volumineuses
    Abstract: As data continues to grow and become more complex, organizations seek innovative solutions to manage their data effectively. Data Mesh is one solution that provides a new approach to managing data in complex organizations. This practical guide offers step-by-step guidance on how to implement data mesh in your organization. In this book, Jean-Georges Perrin and Eric Broda focus on the key components of data mesh and provide practical advice supported by code. You'll explore a simple and intuitive process for identifying key data mesh components and data products, and learn about a consistent set of interfaces and access methods that make data products easy to consume.
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  • 39
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    Convergence of cloud with AI for big data analytics : foundations and innovation (2023)
    Hoboken, NJ : Scrivener Publishing
    Details
    ISBN: 9781119905233 , 1119905230 , 9781119905226 , 1119905222 , 9781119905219 , 1119905214 , 9781119904885
    Language: English
    Pages: 1 online resource.
    Parallel Title: Erscheint auch als
    DDC: 004.67/82
    Keywords: Cloud computing ; Artificial intelligence ; Big data
    Abstract: CONVERGENCE of CLOUD with AI for BIG DATA ANALYTICS This book covers the foundations and applications of cloud computing, AI, and Big Data and analyses their convergence for improved development and services. The 17 chapters of the book masterfully and comprehensively cover the intertwining concepts of artificial intelligence, cloud computing, and big data, all of which have recently emerged as the next-generation paradigms. There has been rigorous growth in their applications and the hybrid blend of AI Cloud and IoT (Ambient-intelligence technology) also relies on input from wireless devices. Despite the multitude of applications and advancements, there are still some limitations and challenges to overcome, such as security, latency, energy consumption, service allocation, healthcare services, network lifetime, etc. Convergence of Cloud with AI for Big Data Analytics: Foundations and Innovation details all these technologies and how they are related to state-of-the-art applications, and provides a comprehensive overview for readers interested in advanced technologies, identifying the challenges, proposed solutions, as well as how to enhance the framework. Audience Researchers and post-graduate students in computing as well as engineers and practitioners in software engineering, electrical engineers, data analysts, and cyber security professionals.
    Note: Includes bibliographical references and index. - Online resource; title from PDF title page (John Wiley, viewed February 15, 2023)
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  • 40
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    Learn Azure Synapse Data Explorer : A Guide to Building Real-Time Analytics Solutions to Unlock Log and Telemetry Data (2023)
    Birmingham : Packt Publishing, Limited
    Details
    ISBN: 9781803239613 , 1803239611
    Language: English
    Pages: 1 online resource (346 p.)
    Edition: 1st edition.
    DDC: 005.7
    Keywords: Big data ; Cloud computing
    Abstract: A hands-on guide to working on use cases helping you ingest, analyze, and serve insightful data from IoT as well as telemetry data sources using Azure Synapse Data Explorer Free PDF included with this book Key Features Augment advanced analytics projects with your IoT and application data Expand your existing Azure Synapse environments with unstructured data Build industry-level projects on integration, experimentation, and dashboarding with Azure Synapse Book Description Large volumes of data are generated daily from applications, websites, IoT devices, and other free-text, semi-structured data sources. Azure Synapse Data Explorer helps you collect, store, and analyze such data, and work with other analytical engines, such as Apache Spark, to develop advanced data science projects and maximize the value you extract from data. This book offers a comprehensive view of Azure Synapse Data Explorer, exploring not only the core scenarios of Data Explorer but also how it integrates within Azure Synapse. From data ingestion to data visualization and advanced analytics, you'll learn to take an end-to-end approach to maximize the value of unstructured data and drive powerful insights using data science capabilities. With real-world usage scenarios, you'll discover how to identify key projects where Azure Synapse Data Explorer can help you achieve your business goals. Throughout the chapters, you'll also find out how to manage big data as part of a software as a service (SaaS) platform, as well as tune, secure, and serve data to end users. By the end of this book, you'll have mastered the big data life cycle and you'll be able to implement advanced analytical scenarios from raw telemetry and log data. What you will learn Integrate Data Explorer pools with all other Azure Synapse services Create Data Explorer pools with Azure Synapse Studio and Azure Portal Ingest, analyze, and serve data to users using Azure Synapse pipelines Integrate Power BI and visualize data with Synapse Studio Configure Azure Machine Learning integration in Azure Synapse Manage cost and troubleshoot Data Explorer pools in Synapse Analytics Secure Synapse workspaces and grant access to Data Explorer pools Who this book is for If you are a data engineer, data analyst, or business analyst working with unstructured data and looking to learn how to maximize the value of such data, this book is for you. If you already have experience working with Azure Synapse and want to incorporate unstructured data into your data science project, you'll also find plenty of useful information in this book. To maximize your learning experience, familiarity with data and performing simple queries using SQL or KQL is recommended. Basic knowledge of Python will help you get more from the examples.
    Note: Description based upon print version of record. - Choosing a data load strategy
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  • 41
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    THE KAGGLE WORKBOOK : self-learning exercises and valuable insights for Kaggle data science competitions (2023)
    Birmingham, UK : Packt Publishing Ltd.
    Details
    ISBN: 9781804610114 , 1804610119 , 9781804611210
    Language: English
    Pages: 1 online resource
    Parallel Title: Erscheint auch als
    DDC: 006.3/1
    Keywords: Machine learning Problems, exercises, etc ; Big data Problems, exercises, etc ; Big data ; Machine learning ; Problems and exercises ; Problems and exercises
    Abstract: Move up the Kaggle leaderboards and supercharge your data science and machine learning career by analyzing famous competitions and working through exercises. Purchase of the print or Kindle book includes a free eBook in PDF format. Key Features Challenge yourself to start thinking like a Kaggle Grandmaster Fill your portfolio with impressive case studies that will come in handy during interviews Packed with exercises and notes pages for you to enhance your skills and record key findings Book Description More than 80,000 Kaggle novices currently participate in Kaggle competitions. To help them navigate the often-overwhelming world of Kaggle, two Grandmasters put their heads together to write The Kaggle Book, which made plenty of waves in the community. Now, they've come back with an even more practical approach based on hands-on exercises that can help you start thinking like an experienced data scientist. In this book, you'll get up close and personal with four extensive case studies based on past Kaggle competitions. You'll learn how bright minds predicted which drivers would likely avoid filing insurance claims in Brazil and see how expert Kagglers used gradient-boosting methods to model Walmart unit sales time-series data. Get into computer vision by discovering different solutions for identifying the type of disease present on cassava leaves. And see how the Kaggle community created predictive algorithms to solve the natural language processing problem of subjective question-answering. You can use this workbook as a supplement alongside The Kaggle Book or on its own alongside resources available on the Kaggle website and other online communities. Whatever path you choose, this workbook will help make you a formidable Kaggle competitor. What you will learn Take your modeling to the next level by analyzing different case studies Boost your data science skillset with a curated selection of exercises Combine different methods to create better solutions Get a deeper insight into NLP and how it can help you solve unlikely challenges Sharpen your knowledge of time-series forecasting Challenge yourself to become a better data scientist Who this book is for If you're new to Kaggle and want to sink your teeth into practical exercises, start with The Kaggle Book, first. A basic understanding of the Kaggle platform, along with knowledge of machine learning and data science is a prerequisite. This book is suitable for anyone starting their Kaggle journey or veterans trying to get better at it. Data analysts/scientists who want to do better in Kaggle competitions and secure jobs with tech giants will find this book helpful.
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  • 42
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    Data Modeling with Snowflake : A Practical Guide to Accelerating Snowflake Development Using Universal Data Modeling Techniques (2023)
    Birmingham : Packt Publishing, Limited
    Details
    ISBN: 9781837632787 , 1837632782
    Language: English
    Pages: 1 online resource (324 p.)
    Parallel Title: Erscheint auch als
    DDC: 005.7
    Keywords: Big data ; Cloud computing
    Abstract: Discover how Snowflake's unique objects and features can be used to leverage universal modeling techniques through real-world examples and SQL recipes Purchase of the print or Kindle book includes a free PDF eBook Key Features Learn core modeling techniques tied to practical examples using native Snowflake architecture Adopt a universal modeling language to communicate business value to functional teams Go beyond physical modeling with SQL recipes to transform and shape your Snowflake data Book Description The Snowflake Data Cloud is one of the fastest-growing platforms for data warehousing and application workloads. Snowflake's scalable, cloud-native architecture and expansive set of features and objects enables you to deliver data solutions quicker than ever before. Yet, we must ensure that these solutions are developed using recommended design patterns and accompanied by documentation that's easily accessible to everyone in the organization. This book will help you get familiar with simple and practical data modeling frameworks that accelerate agile design and evolve with the project from concept to code. These universal principles have helped guide database design for decades, and this book pairs them with unique Snowflake-native objects and examples like never before - giving you a two-for-one crash course in theory as well as direct application. By the end of this Snowflake book, you'll have learned how to leverage Snowflake's innovative features, such as time travel, zero-copy cloning, and change-data-capture, to create cost-effective, efficient designs through time-tested modeling principles that are easily digestible when coupled with real-world examples. What you will learn Discover the time-saving features and applications of data modeling Explore Snowflake's cloud-native architecture and features Understand and apply modeling concepts, techniques, and language using Snowflake objects Master modeling concepts such as normalization and slowly changing dimensions Get comfortable reading and transforming semi-structured data Work directly with pre-built recipes and examples Apply modeling frameworks from Star to Data Vault Who this book is for This book is for developers working with SQL who are looking to build a strong foundation in modeling best practices and gain an understanding of where they can be effectively applied to save time and effort. Whether you're an ace in SQL logic or starting out in database design, this book will equip you with the practical foundations of data modeling to guide you on your data journey with Snowflake. Developers who've recently discovered Snowflake will be able to uncover its core features and learn to incorporate them into universal modeling frameworks.
    Note: Description based upon print version of record
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    Building an event-driven data mesh : patterns for designing and building event-driven architectures (2023)
    Sebastopol, California : O'Reilly Media, Inc.
    Details
    Language: English
    Pages: 1 online resource (259 pages) , illustrations
    Edition: [First edition].
    DDC: 005.7
    Keywords: Big data ; Database management ; Business enterprises Data processing ; Information technology Management
    Abstract: The exponential growth of data combined with the need to derive real-time business value is a critical issue today. An event-driven data mesh can power real-time operational and analytical workloads, all from a single set of data product streams. With practical real-world examples, this book shows you how to successfully design and build an event-driven data mesh.
    Note: Includes index
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  • 44
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    Exploratory data analysis with Python cookbook : over 50 recipes to analyze, visualize, and extract insights from structured and unstructured data (2023)
    Birmingham : Packt Publishing
    Details
    ISBN: 9781803246130 , 1803246138 , 9781803231105
    Language: English
    Pages: 1 online resource
    Edition: 1st edition.
    Parallel Title: Erscheint auch als
    DDC: 005.7
    Keywords: Big data ; Python (Computer program language) ; Big data ; Python (Computer program language)
    Note: Table of ContentsProduct Information DocumentGenerating Summary StatisticsPreparing Data for EDAVisualising Data in PythonPerforming Univariate Analysis in PythonPerforming Bivariate analysis in PythonPerforming Multivariate analysis in PythonAnalysing Time Series dataAnalysing Text dataDealing with Outliers and Missing valuesPerforming Automated EDA in Python. - Description based on CIP data; resource not viewed
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  • 45
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    STREAMING DATA MESH : a model for optimizing real -time data services (2023)
    [Erscheinungsort nicht ermittelbar] : O'REILLY MEDIA
    Details
    ISBN: 9781098130695 , 1098130693
    Language: English
    Pages: 1 online resource
    Parallel Title: Erscheint auch als
    DDC: 005.7
    Keywords: Big data ; Database management ; Business enterprises Data processing ; Information technology Management
    Abstract: Data lakes and warehouses have become increasingly fragile, costly, and difficult to maintain as data gets bigger and moves faster. Data meshes can help your organization decentralize data, giving ownership back to the engineers who produced it. This book provides a concise yet comprehensive overview of data mesh patterns for streaming and real-time data services. Authors Hubert Dulay and Stephen Mooney examine the vast differences between streaming and batch data meshes. Data engineers, architects, data product owners, and those in DevOps and MLOps roles will learn steps for implementing a streaming data mesh, from defining a data domain to building a good data product. Through the course of the book, you'll create a complete self-service data platform and devise a data governance system that enables your mesh to work seamlessly. With this book, you will: Design a streaming data mesh using Kafka Learn how to identify a domain Build your first data product using self-service tools Apply data governance to the data products you create Learn the differences between synchronous and asynchronous data services Implement self-services that support decentralized data.
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  • 46
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    Geospatial Data Analytics on AWS : Discover How to Manage and Analyze Geospatial Data in the Cloud (2023)
    Birmingham : Packt Publishing, Limited
    Details
    ISBN: 9781804610572 , 1804610577
    Language: English
    Pages: 1 online resource (276 p.)
    Edition: 1st edition.
    DDC: 910.285
    Keywords: Geospatial data ; Big data
    Abstract: Build an end-to-end geospatial data lake in AWS using popular AWS services such as RDS, Redshift, DynamoDB, and Athena to manage geodata Purchase of the print or Kindle book includes a free PDF eBook. Key Features Explore the architecture and different use cases to build and manage geospatial data lakes in AWS Discover how to leverage AWS purpose-built databases to store and analyze geospatial data Learn how to recognize which anti-patterns to avoid when managing geospatial data in the cloud Book Description Managing geospatial data and building location-based applications in the cloud can be a daunting task. This comprehensive guide helps you overcome this challenge by presenting the concept of working with geospatial data in the cloud in an easy-to-understand way, along with teaching you how to design and build data lake architecture in AWS for geospatial data. You'll begin by exploring the use of AWS databases like Redshift and Aurora PostgreSQL for storing and analyzing geospatial data. Next, you'll leverage services such as DynamoDB and Athena, which offer powerful built-in geospatial functions for indexing and querying geospatial data. The book is filled with practical examples to illustrate the benefits of managing geospatial data in the cloud. As you advance, you'll discover how to analyze and visualize data using Python and R, and utilize QuickSight to share derived insights. The concluding chapters explore the integration of commonly used platforms like Open Data on AWS, OpenStreetMap, and ArcGIS with AWS to enable you to optimize efficiency and provide a supportive community for continuous learning. By the end of this book, you'll have the necessary tools and expertise to build and manage your own geospatial data lake on AWS, along with the knowledge needed to tackle geospatial data management challenges and make the most of AWS services. What you will learn Discover how to optimize the cloud to store your geospatial data Explore management strategies for your data repository using AWS Single Sign-On and IAM Create effective SQL queries against your geospatial data using Athena Validate postal addresses using Amazon Location services Process structured and unstructured geospatial data efficiently using R Use Amazon SageMaker to enable machine learning features in your application Explore the free and subscription satellite imagery data available for use in your GIS Who this book is for If you understand the importance of accurate coordinates, but not necessarily the cloud, then this book is for you. This book is best suited for GIS developers, GIS analysts, data analysts, and data scientists looking to enhance their solutions with geospatial data for cloud-centric applications. A basic understanding of geographic concepts is suggested, but no experience with the cloud is necessary for understanding the concepts in this book.
    Note: Description based upon print version of record
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  • 47
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    Data mesh in action (2023)
    [Shelter Island, New York] : Manning Publications
    Details
    Language: English
    Pages: 1 online resource (1 audio file (9 hr., 35 min.))
    Edition: [First edition].
    DDC: 658.4/038011
    Keywords: Management information systems ; Business Data processing ; Big data ; Audiobooks
    Abstract: Revolutionize the way your organization approaches data with a data mesh! This new decentralized architecture outpaces monolithic lakes and warehouses and can work for a company of any size. In Data Mesh in Action you will learn how to: Implement a data mesh in your organization Turn data into a data product Move from your current data architecture to a data mesh Identify data domains, and decompose an organization into smaller, manageable domains Set up the central governance and local governance levels over data Balance responsibilities between the two levels of governance Establish a platform that allows efficient connection of distributed data products and automated governance Data Mesh in Action reveals how this groundbreaking architecture looks for both startups and large enterprises. You won't need any new technology--this book shows you how to start implementing a data mesh with flexible processes and organizational change. You'll explore both an extended case study and real-world examples. As you go, you'll be expertly guided through discussions around Socio-Technical Architecture and Domain-Driven Design with the goal of building a sleek data-as-a-product system. Plus, dozens of workshop techniques for both in-person and remote meetings help you onboard colleagues and drive a successful transition. About the Technology Business increasingly relies on efficiently storing and accessing large volumes of data. The data mesh is a new way to decentralize data management that radically improves security and discoverability. A well-designed data mesh simplifies self-service data consumption and reduces the bottlenecks created by monolithic data architectures. About the Book Data Mesh in Action teaches you pragmatic ways to decentralize your data and organize it into an effective data mesh. You'll start by building a minimum viable data product, which you'll expand into a self-service data platform, chapter-by-chapter. You'll love the book's unique "sliders" that adjust the mesh to meet your specific needs. You'll also learn processes and leadership techniques that will change the way you and your colleagues think about data. What's Inside Decompose an organization into manageable domains Turn data into a data product Set up central and local governance levels Build a fit-for-purpose data platform Improve management, initiation, and support techniques About the Reader For data professionals. Requires no specific programming stack or data platform. About the Authors Jacek Majchrzak is a hands-on lead data architect. Dr. Sven Balnojan manages data products and teams. Dr. Marian Siwiak is a data scientist and a management consultant for IT, scientific, and technical projects. Quotes This book teleports you into the seat of the chief architect on a data mesh project. - From the Foreword by Jean-Georges Perrin, PayPal A must-read for anyone who works in data. - Prukalpa Sankar, Co-Founder of Atlan Satisfies all those 'what', 'why', and 'how' questions. A unique blend of process and technology, and an excellent, example-driven resource. - Shiroshica Kulatilake, WSO2 The starting point for your journey in the new generation of data platforms. - Arnaud Castelltort, University of Montpellier
    Note: Online resource; title from title details screen (O'Reilly, viewed October 3, 2023)
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    AI for big data-based engineering applications from security perspectives (2023)
    Boca Raton : CRC Press
    Details
    ISBN: 9781003230113 , 1003230113 , 9781000901504 , 1000901505 , 9781000901559 , 1000901556
    Language: English
    Pages: 1 online resource
    Edition: First edition.
    Parallel Title: Erscheint auch als
    Keywords: Integrated circuits Design and construction ; Data processing ; Security measures ; Artificial intelligence Industrial applications ; Big data ; COMPUTERS / Networking / Security ; COMPUTERS / Artificial Intelligence ; COMPUTERS / Data Processing / General
    Abstract: "Artificial Intelligence, machine learning and advanced electronic circuits are learning from every data input and using those inputs to generate new rules for future business analytics. Artificial Intelligence and Machine Learning are now giving us new opportunities to use the big data that we already had, as well as unleash a whole lot of new use cases with new data types. With the increasing use of Artificial Intelligence dealing with highly sensitive information such as health care, adequate security measures are required to be taken for the secure storage and transmission. AI for Big data based Engineering applications from security perspectives provide broader coverage of the basic aspects of advanced circuits design and applications. The book AI for Big data based Engineering applications from security perspectives is an integrated source which aims at understanding the basic concepts associated with the security of the advanced circuits. The content includes theoretical frameworks and recent empirical findings in the field to understand the associated principles, key challenges and recent real-time applications of the advanced circuits, AI and Big data security. It illustrates the notions, models and terminologies that are widely used in the area of VLSI circuits, security, identifies the existing security issues in the field, and evaluates the underlying factors that influence the security of the systems. It emphasizes the idea of understanding the motivation of the advanced circuits' design to establish the AI interface and to mitigate the security attacks in a better way for big data. This book also outlines the exciting areas of future research where the already existing methodologies can be implemented. Moreover, this book is suitable for students, researchers, and professionals who are looking forward to carry out research in the field of AI for Big data based Engineering applications from security perspectives, faculty members across the universities and software developers"--
    Note: Includes bibliographical references. - Description based on print version record and CIP data provided by publisher ; resource not viewed
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  • 49
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    Building ETL Pipelines with Python : Create and Deploy Enterprise-Ready ETL Pipelines by Employing Modern Methods (2023)
    Birmingham : Packt Publishing, Limited
    Details
    ISBN: 9781804615539 , 1804615536 , 9781804615256
    Language: English
    Pages: 1 online resource (246 p.)
    Edition: 1st edition.
    DDC: 005.74
    Keywords: Data mining ; Python (Computer program language) ; Big data ; Electronic data processing
    Abstract: Develop production-ready ETL pipelines by leveraging Python libraries and deploying them for suitable use cases Key Features Understand how to set up a Python virtual environment with PyCharm Learn functional and object-oriented approaches to create ETL pipelines Create robust CI/CD processes for ETL pipelines Purchase of the print or Kindle book includes a free PDF eBook Book Description Modern extract, transform, and load (ETL) pipelines for data engineering have favored the Python language for its broad range of uses and a large assortment of tools, applications, and open source components. With its simplicity and extensive library support, Python has emerged as the undisputed choice for data processing. In this book, you'll walk through the end-to-end process of ETL data pipeline development, starting with an introduction to the fundamentals of data pipelines and establishing a Python development environment to create pipelines. Once you've explored the ETL pipeline design principles and ET development process, you'll be equipped to design custom ETL pipelines. Next, you'll get to grips with the steps in the ETL process, which involves extracting valuable data; performing transformations, through cleaning, manipulation, and ensuring data integrity; and ultimately loading the processed data into storage systems. You'll also review several ETL modules in Python, comparing their pros and cons when building data pipelines and leveraging cloud tools, such as AWS, to create scalable data pipelines. Lastly, you'll learn about the concept of test-driven development for ETL pipelines to ensure safe deployments. By the end of this book, you'll have worked on several hands-on examples to create high-performance ETL pipelines to develop robust, scalable, and resilient environments using Python. What you will learn Explore the available libraries and tools to create ETL pipelines using Python Write clean and resilient ETL code in Python that can be extended and easily scaled Understand the best practices and design principles for creating ETL pipelines Orchestrate the ETL process and scale the ETL pipeline effectively Discover tools and services available in AWS for ETL pipelines Understand different testing strategies and implement them with the ETL process Who this book is for If you are a data engineer or software professional looking to create enterprise-level ETL pipelines using Python, this book is for you. Fundamental knowledge of Python is a prerequisite.
    Note: Description based upon print version of record. - Includes index. - Precautions to consider
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    MODERN DATA ARCHITECTURES WITH PYTHON : a practical guide to building and deploying data pipelines, data warehouses, and data lakes with Python (2023)
    Birmingham, UK : Packt Publishing Ltd.
    Details
    ISBN: 9781801076418 , 1801076413 , 9781801070492
    Language: English
    Pages: 1 online resource
    Edition: 1st edition.
    Parallel Title: Erscheint auch als
    DDC: 005.13/3
    Keywords: Python (Computer program language) ; Data structures (Computer science) ; Big data
    Abstract: Build scalable and reliable data ecosystems using Data Mesh, Databricks Spark, and Kafka Key Features Develop modern data skills used in emerging technologies Learn pragmatic design methodologies such as Data Mesh and data lakehouses Gain a deeper understanding of data governance Purchase of the print or Kindle book includes a free PDF eBook Book Description Modern Data Architectures with Python will teach you how to seamlessly incorporate your machine learning and data science work streams into your open data platforms. You'll learn how to take your data and create open lakehouses that work with any technology using tried-and-true techniques, including the medallion architecture and Delta Lake. Starting with the fundamentals, this book will help you build pipelines on Databricks, an open data platform, using SQL and Python. You'll gain an understanding of notebooks and applications written in Python using standard software engineering tools such as git, pre-commit, Jenkins, and Github. Next, you'll delve into streaming and batch-based data processing using Apache Spark and Confluent Kafka. As you advance, you'll learn how to deploy your resources using infrastructure as code and how to automate your workflows and code development. Since any data platform's ability to handle and work with AI and ML is a vital component, you'll also explore the basics of ML and how to work with modern MLOps tooling. Finally, you'll get hands-on experience with Apache Spark, one of the key data technologies in today's market. By the end of this book, you'll have amassed a wealth of practical and theoretical knowledge to build, manage, orchestrate, and architect your data ecosystems. What you will learn Understand data patterns including delta architecture Discover how to increase performance with Spark internals Find out how to design critical data diagrams Explore MLOps with tools such as AutoML and MLflow Get to grips with building data products in a data mesh Discover data governance and build confidence in your data Introduce data visualizations and dashboards into your data practice Who this book is for This book is for developers, analytics engineers, and managers looking to further develop a data ecosystem within their organization. While they're not prerequisites, basic knowledge of Python and prior experience with data will help you to read and follow along with the examples.
    Note: Includes index
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    Data for all (2023)
    [Place of publication not identified] : Manning Publications
    Details
    Language: English
    Pages: 1 online resource (1 video file (7 hr.)) , sound, color.
    Edition: Video edition.
    DDC: 658/.05
    Keywords: Business Data processing ; Data protection ; Big data ; Instructional films ; Nonfiction films ; Internet videos
    Abstract: Do you know what happens to your personal data when you are browsing, buying, or using apps? Discover how your data is harvested and exploited, and what you can do to access, delete, and monetize it. Data for All empowers everyone--from tech experts to the general public--to control how third parties use personal data. Read this eye-opening book to learn: The types of data you generate with every action, every day Where your data is stored, who controls it, and how much money they make from it How you can manage access and monetization of your own data Restricting data access to only companies and organizations you want to support The history of how we think about data, and why that is changing The new data ecosystem being built right now for your benefit The data you generate every day is the lifeblood of many large companies--and they make billions of dollars using it. In Data for All, bestselling author John K. Thompson outlines how this one-sided data economy is about to undergo a dramatic change. Thompson pulls back the curtain to reveal the true nature of data ownership, and how you can turn your data from a revenue stream for companies into a financial asset for your benefit. About the Technology Do you know what happens to your personal data when you're browsing and buying? New global laws are turning the tide on companies who make billions from your clicks, searches, and likes. This eye-opening book provides an inspiring vision of how you can take back control of the data you generate every day. About the Book Data for All gives you a step-by-step plan to transform your relationship with data and start earning a "data dividend"--hundreds or thousands of dollars paid out simply for your online activities. You'll learn how to oversee who accesses your data, how much different types of data are worth, and how to keep private details private. What's Inside The types of data you generate with every action, every day How you can manage access and monetization of your own data The history of how we think about data, and why that is changing The new data ecosystem being built right now for your benefit About the Reader For anyone who is curious or concerned about how their data is used. No technical knowledge required. About the Author John K. Thompson is an international technology executive with over 37 years of experience in the fields of data, advanced analytics, and artificial intelligence. Quotes An honest, direct, pull-no-punches source on one of the most important personal issues of our time....I changed some of my own behaviors after reading the book, and I suggest you do so as well. You have more to lose than you may think. - From the Foreword by Thomas H. Davenport, author of Competing on Analytics and The AI Advantage A must-read for anyone interested in the future of data. It helped me understand the reasons behind the current data ecosystem and the laws that are shaping its future. A great resource for both professionals and individuals. I highly recommend it. - Ravit Jain, Founder & Host of The Ravit Show, Data Science Evangelist.
    Note: Online resource; title from title details screen (O'Reilly, viewed November 15, 2023)
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    Fundamentals of data observability : implement trustworthy end-to-end data solutions (2023)
    Sebastopol, CA : O'Reilly Media, Inc.
    Details
    ISBN: 9781098133269 , 1098133269
    Language: English
    Pages: 1 online resource (264 pages) , illustrations
    Edition: First edition.
    Parallel Title: Erscheint auch als
    DDC: 005.7
    Keywords: Electronic data processing ; Big data
    Abstract: Quickly detect, troubleshoot, and prevent a wide range of data issues through data observability, a set of best practices that enables data teams to gain greater visibility of data and its usage. If you're a data engineer, data architect, or machine learning engineer who depends on the quality of your data, this book shows you how to focus on the practical aspects of introducing data observability in your everyday work. Author Andy Petrella helps you build the right habits to identify and solve data issues, such as data drifts and poor quality, so you can stop their propagation in data applications, pipelines, and analytics. You'll learn ways to introduce data observability, including setting up a framework for generating and collecting all the information you need.
    Note: Includes index
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    Databricks certified data engineer associate (2023)
    [Place of publication not identified] : Pragmatic AI Solutions
    Details
    Language: English
    Pages: 1 online resource (1 video file (2 hr., 25 min.)) , sound, color.
    Edition: [First edition].
    DDC: 005.74
    Keywords: Databases ; Big data ; Instructional films ; Nonfiction films ; Internet videos
    Abstract: Databricks Certified Data Engineer Associate Course 1: Databricks Lakehouse Platform Description Learn foundational Databricks capabilities including compute, storage, notebooks, and jobs to build scalable data solutions. Learning Objectives Create clusters and configure runtime environments Perform exploratory analysis with notebooks Schedule and monitor multi-task workflows Course 2: Databricks SQL Description Master Spark SQL for reading, transforming, and loading data at scale. Learn techniques like data validation, custom business logic, and slowly changing dimensions. Learning Objectives Query data in notebooks with Spark SQL Handle complex data types Apply data quality rules Implement slowly changing dimensions Course 3: Databricks ML Description Build ML models with Python and Scala APIs in Databricks. Learn best practices for feature engineering, hyperparameter tuning, and model evaluation. Learning Objectives Engineer features from raw data Tune models with cross validation Evaluate model performance Operationalize models with MLflow Course 4: Databricks Data Engineering Description Architect reliable and performant data infrastructure with Delta Lake, streaming, and autoscaling. Learning Objectives Implement ACID transactions Build streaming ETL solutions Autoscale infrastructure to meet SLAs Migrate data warehouses to lakehouse Course 5: Workloads with Jobs Description Orchestrate workloads using multi-task Jobs with configurable scheduling, dependencies, and error handling. Learning Objectives Schedule notebooks, jobs and pipelines Set dependencies across tasks Handle and retry failures Monitor runs using the Jobs UI Course 6: Data Access with Unity Catalog Description Provide governed data access across storage like ADLS, S3, and GCS using Unity Catalog. Learning Objectives Deploy a Unity Catalog Manage credentials securely Apply object-level security Query data from storage tiers Additional Popular Resources Assimilate OpenAI 52 Weeks of AWS-The Complete Series Microsoft Azure Fundamentals (AZ-900) Certification Rust Bootcamp Python Bootcamp Google Professional Machine Learning Engineer Course 2023 (Rough Draft) Rust Data Engineering Building with the GitHub EcoSystem: Copilot, CodeSpaces, and GitHub Actions Microsoft Azure Fundamentals (AZ-900) Certification Google Professional Cloud Architect Certification Course 2023 (Rough Draft) AWS Solutions Architect Professional (SAP-C02) 2023.
    Note: "Pragmatic AI Labs course.". - Online resource; title from title details screen (O'Reilly, viewed Decenber 19, 2023)
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    R for data science : import, tidy, transform, visualize, and model data (2023)
    Sebastopol, CA : O'Reilly Media, Inc.
    Details
    Language: English
    Pages: 1 online resource (576 pages) , illustrations
    Edition: 2nd edition.
    DDC: 006.3/12
    Keywords: Databases ; Data mining Computer programs ; Information visualization Computer programs ; R (Computer program language) ; Big data ; Exploration de données (Informatique) - Logiciels ; Visualisation de l'information - Logiciels ; R (Langage de programmation) ; Données volumineuses ; Big data ; R (Computer program language) ; Electronic data processing ; Information visualization ; Statistics
    Abstract: Use R to turn data into insight, knowledge, and understanding. With this practical book, aspiring data scientists will learn how to do data science with R and RStudio, along with the tidyverse—a collection of R packages designed to work together to make data science fast, fluent, and fun. Even if you have no programming experience, this updated edition will have you doing data science quickly. You'll learn how to import, transform, and visualize your data and communicate the results. And you'll get a complete, big-picture understanding of the data science cycle and the basic tools you need to manage the details. Updated for the latest tidyverse features and best practices, new chapters show you how to get data from spreadsheets, databases, and websites. Exercises help you practice what you've learned along the way.
    Note: Includes bibliographical references and index
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    THE ENTERPRISE DATA CATALOG : improve data discovery, ensure data governance, and enable innovation (2023)
    Sebastopol, California : O'Reilly Media, Inc.
    Details
    ISBN: 9781492098683 , 149209868X
    Language: English
    Pages: 1 online resource
    Parallel Title: Erscheint auch als
    DDC: 005.74
    Keywords: Metadata ; Database management ; Big data
    Abstract: Combing the web is simple, but how do you search for data at work? It's difficult and time-consuming, and can sometimes seem impossible. This book introduces a practical solution: the data catalog. Data analysts, data scientists, and data engineers will learn how to create true data discovery in their organizations, making the catalog a key enabler for data-driven innovation and data governance.
    URL: lizenzpflichtig
    URL: Inhaltsverzeichnis
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    Digital China: Big Data and Government Managerial Decision (2023)
    Singapore : Springer Nature Singapore | Cham : Springer International Publishing AG
    Details
    ISBN: 9789811997150 , 9811997152
    Language: English
    Pages: 1 Online-Ressource (XIII, 141 Seiten) , 19 illus., 1 illus. in color.
    Edition: 1st ed. 2023
    Parallel Title: Erscheint auch als Digital China: Big Data and Government Managerial Decision
    DDC: 303,483
    Keywords: Science Social aspects ; Big data ; Social policy ; Science and Technology Studies ; Big Data ; Social Policy ; Aufsatzsammlung ; Aufsatzsammlung
    DOI: 10.1007/978-981-19-9715-0
    URL: https://doi.org/10.1007/978-981-19-9715-0
    URL: Inhaltsverzeichnis
    URL: Ausführliche Beschreibung
    URL: Volltext  (URL des Erstveröffentlichers)
    URL: Volltext  (URL des Erstveröffentlichers)
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    Fundamentals of Data Engineering (2022)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (82 pages)
    Edition: 1st edition
    DDC: 004.6782
    Keywords: Database management ; Big data ; Data mining ; Data Mining ; Electronic books ; Données volumineuses ; Exploration de données (Informatique) ; Big data ; Data mining ; Database management ; Bases de données ; Gestion
    Abstract: Data engineering has grown rapidly in the past decade, leaving many software engineers, data scientists, and analysts looking for a comprehensive view of this practice. With this practical book, you'll learn how to plan and build systems to serve the needs of your organization and customers by evaluating the best technologies available in the framework of the data engineering lifecycle. Authors Joe Reis and Matt Housley walk you through the data engineering lifecycle and show you how to stitch together a variety of cloud technologies to serve the needs of downstream data consumers. You'll understand how to apply the concepts of data generation, ingestion, orchestration, transformation, storage, governance, and deployment that are critical in any data environment regardless of the underlying technology. This book will help you: Assess data engineering problems using an end-to-end data framework of best practices Cut through marketing hype when choosing data technologies, architecture, and processes Use the data engineering lifecycle to design and build a robust architecture Incorporate data governance and security across the data engineering lifecycle
    Note: Online resource; Title from title page (viewed September 25, 2022) , Mode of access: World Wide Web.
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    Azure Databricks, Pandas, and open datasets (2022)
    [Place of publication not identified] : Pragmatic AI Solutions
    Details
    Language: English
    Pages: 1 online resource (1 video file (9 min.)) , sound, color.
    Edition: [First edition].
    DDC: 005.7
    Keywords: Big data ; Microsoft Azure (Computing platform) ; Données volumineuses ; Instructional films ; Nonfiction films ; Internet videos ; Films de formation ; Films autres que de fiction ; Vidéos sur Internet ; Webcast
    Abstract: Azure Databricks with Pandas and Open Datasets. Find out how to get a working cluster with Databricks using Azure and then use the full Pandas API operating in the cluster with Open Datasets and a Python Jupyter Notebook. This video will walk you through creating a workspace in Azure to create the Databricks service, then create the cluster that comes with the Pyspark Pandas API, and finally import the open datasets into the cluster. Although straightforward to create a Databricks cluster with Azure, it is a bit more involved to run a Python Jupyter Notebook that has Azure ML Open Datasets installed and availabe in the cluster along with the ability to use the full Pandas API you are used to working with and taking advantage of the clustering capabilities from Databricks.
    Note: Online resource; title from title details screen (O’Reilly, viewed March 10, 2022)
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    Simplify Big Data Analytics with Amazon EMR : A Beginner's Guide to Learning and Implementing Amazon EMR for Building Data Analytics Solutions (2022)
    Birmingham : Packt Publishing, Limited
    Details
    ISBN: 9781801077729 , 180107772X
    Language: English
    Pages: 1 online resource (430 p.)
    DDC: 005.7
    Keywords: MapReduce (Computer file) ; Big data ; Données volumineuses ; Electronic books
    Abstract: Design scalable big data solutions using Hadoop, Spark, and AWS cloud native services Key Features Build data pipelines that require distributed processing capabilities on a large volume of data Discover the security features of EMR such as data protection and granular permission management Explore best practices and optimization techniques for building data analytics solutions in Amazon EMR Book Description Amazon EMR, formerly Amazon Elastic MapReduce, provides a managed Hadoop cluster in Amazon Web Services (AWS) that you can use to implement batch or streaming data pipelines. By gaining expertise in Amazon EMR, you can design and implement data analytics pipelines with persistent or transient EMR clusters in AWS. This book is a practical guide to Amazon EMR for building data pipelines. You'll start by understanding the Amazon EMR architecture, cluster nodes, features, and deployment options, along with their pricing. Next, the book covers the various big data applications that EMR supports. You'll then focus on the advanced configuration of EMR applications, hardware, networking, security, troubleshooting, logging, and the different SDKs and APIs it provides. Later chapters will show you how to implement common Amazon EMR use cases, including batch ETL with Spark, real-time streaming with Spark Streaming, and handling UPSERT in S3 Data Lake with Apache Hudi. Finally, you'll orchestrate your EMR jobs and strategize on-premises Hadoop cluster migration to EMR. In addition to this, you'll explore best practices and cost optimization techniques while implementing your data analytics pipeline in EMR. By the end of this book, you'll be able to build and deploy Hadoop- or Spark-based apps on Amazon EMR and also migrate your existing on-premises Hadoop workloads to AWS. What you will learn Explore Amazon EMR features, architecture, Hadoop interfaces, and EMR Studio Configure, deploy, and orchestrate Hadoop or Spark jobs in production Implement the security, data governance, and monitoring capabilities of EMR Build applications for batch and real-time streaming data analytics solutions Perform interactive development with a persistent EMR cluster and Notebook Orchestrate an EMR Spark job using AWS Step Functions and Apache Airflow Who this book is for This book is for data engineers, data analysts, data scientists, and solution architects who are interested in building data analytics solutions with the Hadoop ecosystem services and Amazon EMR. Prior experience in either Python programming, Scala, or the Java programming language and a basic understanding of Hadoop and AWS will help you make the most out of this book.
    Note: Description based upon print version of record
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    Product analytics for data-driven decisions : derive insights from web analytics data (2022)
    [Place of publication not identified] : Addison-Wesley Professional
    Details
    ISBN: 9780137907748 , 0137907745
    Language: English
    Pages: 1 online resource (1 video file (7 hr., 17 min.)) , sound, color.
    Edition: [First edition].
    Series Statement: Live lessons
    DDC: 658/.05
    Keywords: Business enterprises Data processing ; Machine learning ; Web usage mining ; Big data ; Instructional films ; Nonfiction films ; Internet videos
    Abstract: 7+ Hours of Video Instruction Learn How to Work with Real-World Data to Derive Actionable Business Insights Overview Product Analytics for Data-Driven Decisions: Derive Insights from Web Analytics Data will explore core concepts that will help viewers work with their data, identify bias in data sets, differentiate good data from bad data, and ultimately derive insights to help make actionable business decisions. Learners will see real-world examples of successful product analytics and learn how to utilize qualitative and quantitative measures for desirable outcomes. Instructor Joanne Rodrigues is an accomplished data scientist, enterprise manager, and entrepreneur who applies machine learning/statistical algorithms to business strategy. Through eight unique video lessons, Rodrigues will provide in-depth training in the data generating process, psychological and neurological theories of behavior, implementing statistical tools in survey design and psychometric techniques, and much more. What You Will Learn Identify and create good metrics and KPIs to drive growth Avoid common pitfalls in understanding your data Move from raw data to inference and strategy Who Should Take This Course? Product, Consumer, or User Data Scientists Product, Marketing, Research or Business Analysts Entrepreneurs or Business Owners Course Requirements There is no prior knowledge or requirements for this course. About Pearson Video Training Pearson publishes expert-led video tutorials covering a wide selection of technology topics designed to teach you the skills you need to succeed. These professional and personal technology videos feature world-leading author instructors published by your trusted technology brands: Addison-Wesley, Cisco Press, Pearson IT Certification, Sams, and Que Topics include: IT Certification, Network Security, Cisco Technology, Programming, Web Development, Mobile Development, and more. Learn more about Pearson Video training at http://www.informit.com/video.
    Note: Online resource; title from title details screen (O'Reilly, viewed July 6, 2022)
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    Building an end-to-end batch data pipeline with Apache Spark (2022)
    [Place of publication not identified] : Manning Publications
    Details
    Language: English
    Pages: 1 online resource (1 video file (38 min.)) , sound, color.
    Edition: [First edition].
    DDC: 004/.36
    Keywords: Spark (Electronic resource : Apache Software Foundation) ; Electronic data processing Distributed processing ; Big data ; Instructional films ; Nonfiction films ; Internet videos
    Abstract: Explore Big Data architectures and the tools you can leverage to build an end-to-end data platform. Learn about data ingestion using Apache Spark.
    Note: Online resource; title from title details screen (O'Reilly, viewed July 6, 2022)
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    Snowflake : build and architect data pipelines using AWS (2022)
    [Place of publication not identified] : Packt Publishing
    Details
    ISBN: 9781804615676 , 1804615676
    Language: English
    Pages: 1 online resource (1 video file (7 hr., 38 min.)) , sound, color.
    Edition: [First edition].
    DDC: 004.67/82
    Keywords: Amazon Web Services (Firm) ; Cloud computing ; Computer architecture ; Big data ; Instructional films ; Nonfiction films ; Internet videos
    Abstract: Be a part of a comprehensive course on AWS and Snowflake integration. Leverage your learning with a power-packed and well-structured course with an adequate pace. About This Video Learn from an easy-to-understand and step-by-step course, divided into 85+ videos along with detailed resource files Integrate real-time streaming data and data orchestration with Airflow and Snowflake Highly practical explanations and lab exercises to help you grasp the most out of the course In Detail Snowflake is the next big thing, and it is becoming a full-blown data ecosystem. With the level of scalability and efficiency in handling massive volumes of data and also with several new concepts in it, this is the right time to wrap your head around Snowflake and have it in your toolkit. This course not only covers the core features of Snowflake but also teaches you how to deploy Python/PySpark jobs in AWS Glue and Airflow that communicate with Snowflake, which is one of the most important aspects of building pipelines. In this course, you will look at Snowflake, and then the most crucial aspects of Snowflake in an efficient manner. You will be writing Python/Spark Jobs in AWS Glue Jobs for data transformation and seeing real-time streaming using Kafka and Snowflake. You will be interacting with external functions and use cases, and finally, see the security features in Snowflake. By the end of this course, you will have learned about Snowflake and learned how to build and architect data pipelines using AWS. You need to have an active AWS account in order to perform the sections related to Python and PySpark. For the rest of the course, a free trial Snowflake account should suffice. Audience This course is ideal for Software engineers, aspiring data engineers or data analysts, and data scientists who want to excel in their careers in the IT domain. Apart from them, this course is also good for programmers and database administrators with experience in writing SQL queries. A prior programming experience in SQL or at least some prior knowledge in writing queries and Python is a must. You should have a basic experience or understanding of cloud services such as AWS is important along with an active AWS account.
    Note: Online resource; title from title details screen (O'Reilly, viewed October 18, 2022)
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  • 63
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    Online Resource
    Microsoft Azure data lake storage service : (Gen1 and Gen2) (2022)
    [Birmingham, United Kingdom] : Packt Publishing
    Details
    Language: English
    Pages: 1 online resource (1 video file (6 hr., 15 min.)) , sound, color.
    Edition: [First edition].
    DDC: 004.165
    Keywords: Microsoft .NET Framework ; Microsoft Azure (Computing platform) ; Big data ; Données volumineuses ; Instructional films ; Nonfiction films ; Internet videos ; Films de formation ; Films autres que de fiction ; Vidéos sur Internet ; Webcast
    Abstract: Learn to ingest, process, and export data in Azure Data Lake Storage Service Gen1 and Gen2 using Databricks and HDInsight About This Video Discover Microsoft Azure Data Lake Learn to use Azure Databricks and HDInsight to process data in ADLS Explore data lifecycle and architecture around Data Lake In Detail Azure Data Lake Storage Gen2 (ADLS) is a cloud-based repository for both structured and unstructured data. For example, you could use it to store everything, from documents to images to social media streams. This is one of the most effective ways to go for big data processing; that is, to store your data in ADLS and then process it using Spark, which is a faster version of Hadoop, on Azure Databricks. This is a comprehensive hands-on course for anyone who is interested in Azure's big data analytics services. You will learn hands-on with examples to import data into ADLS and then securely access it and analyze it using Azure Databricks and Azure HDInsight. You will also learn how to monitor and optimize your Data Lake storage. This course provides an end-to-end demonstration for one to have a noticeably clear understanding of Data Lake. By the end of this course, you will learn how to ingest, process, and export data using Databricks and HDInsight. You will have a solid understanding of Microsoft Azure Data Lake Storage Service (Gen1 and Gen2) and its features and properties, which will help you further in your professional endeavors. Audience This course is for anyone interested in Azure's big data analytics services. Also, Microsoft Azure data engineers, database and BI developers, database administrators, data analysts, or similar profiles can opt for this course. Just a basic understanding of data warehouse and database, in general, will help you understand this course better.
    Note: "Updated in February 2022.". - Online resource; title from title details screen (O’Reilly, viewed March 10, 2022)
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  • 64
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    Data superstream : data lakes and warehouses (2022)
    [Place of publication not identified] : O'Reilly Media, Inc.
    Details
    Language: English
    Pages: 1 online resource (1 video file (3 hr., 17 min.)) , sound, color.
    Edition: [First edition].
    DDC: 005.7
    Keywords: Database management ; Management information systems ; Information storage and retrieval systems ; Data warehousing ; Big data ; Cloud computing ; Management Information Systems ; Information Systems ; Bases de données ; Gestion ; Systèmes d'information de gestion ; Systèmes d'information ; Entrepôts de données (Informatique) ; Données volumineuses ; Infonuagique ; Instructional films ; Nonfiction films ; Internet videos ; Films de formation ; Films autres que de fiction ; Vidéos sur Internet ; Webcast
    Abstract: Storing, processing, and moving data in the cloud efficiently and cost-effectively is a must for working with today's enormous datasets. These expert-led sessions will help you gain insight into how to increase the scalability, speed, and availability of your data, along with best practices for utilizing your data warehouse, data lake, or data lakehouse. About the Data Superstream Series: This three-part Superstream series is designed to help your organization maximize the business impact of your data. Each day covers different topics, with unique sessions lasting no more than four hours. And they're packed with insights from key innovators and the latest tools and technologies to help you stay ahead of it all. What you'll learn and how you can apply it Get an overview of the latest technologies for storing and managing your data Learn cutting-edge strategies for optimizing and deploying your cloud data lake Understand how to implement access control to maintain data privacy in your cloud data warehouse Find out how to utilize the lakehouse architecture to support ML and AI applications Discover the benefits of a data mesh approach for addressing data ownership challenges in your organization This recording of a live event is for you because... You're a data or software engineer or solution architect interested in learning about the latest trends in storing, processing, and managing data. You want to improve the scalability, speed, and availability of your data. You want to better understand the systems that you already use and learn how to take full advantage of their capabilities. Recommended follow-up: Read The Enterprise Big Data Lake (book) Read Delta Lake: The Definitive Guide (early release book) Take Data Mesh in Practice (live online training course with Max Schultze and Arif Wider) Please note that slides or supplemental materials are not available for download from this recording. Resources are only provided at the time of the live event.
    Note: Online resource; title from title details screen (O'Reilly, viewed March 9, 2022)
    URL: lizenzpflichtig
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  • 65
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    Building the snowflake data cloud : monetizing and democratizing your data (2022)
    New York, NY : Apress
    Details
    ISBN: 9781484285930 , 148428593X
    Language: English
    Pages: 1 online resource (483 pages) , illustrations (some color)
    Parallel Title: Erscheint auch als
    Keywords: Cloud computing ; Big data ; Electronic books ; Electronic books
    Abstract: Implement the Snowflake Data Cloud using best practices and reap the benefits of scalability and low-cost from the industry-leading, cloud-based, data warehousing platform. This book provides a detailed how-to explanation, and assumes familiarity with Snowflake core concepts and principles. It is a project-oriented book with a hands-on approach to designing, developing, and implementing your Data Cloud with security at the center. As you work through the examples, you will develop the skill, knowledge, and expertise to expand your capability by incorporating additional Snowflake features, tools, and techniques. Your Snowflake Data Cloud will be fit for purpose, extensible, and at the forefront of both Direct Share, Data Exchange, and Snowflake Marketplace. Building the Snowflake Data Cloud helps you transform your organization into monetizing the value locked up within your data. As the digital economy takes hold, with data volume, velocity, and variety growing at exponential rates, you need tools and techniques to quickly categorize, collate, summarize, and aggregate data. You also need the means to seamlessly distribute to release value. This book shows how Snowflake provides all these things and how to use them to your advantage. The book helps you succeed by delivering faster than you can deliver with legacy products and techniques. You will learn how to leverage what you already know, and what you don't, all applied in a Snowflake Data Cloud context. After reading this book, you will discover and embrace the future where the Data Cloud is central. You will be able to position your organization to take advantage by identifying, adopting, and preparing your tooling for the coming wave of opportunity around sharing and monetizing valuable, corporate data. What You Will Learn Understand why Data Cloud is important to the success of your organization Up-skill and adopt Snowflake, leveraging the benefits of cloud platforms Articulate the Snowflake Marketplace and identify opportunities to monetize data Identify tools and techniques to accelerate integration with Data Cloud Manage data consumption by monitoring and controlling access to datasets Develop data load and transform capabilities for use in future projects Who This Book Is For Solution architects seeking implementation patterns to integrate with a Data Cloud; data warehouse developers looking for tips, tools, and techniques to rapidly deliver data pipelines; sales managers who want to monetize their datasets and understand the opportunities that Data Cloud presents; and anyone who wishes to unlock value contained within their data silos.
    Note: Includes index. - Description based on online resource; title from digital title page (viewed on September 21, 2022)
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  • 66
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    Software Architecture Hour with Neal Ford : Building a data mesh with Zhamak Dehghani (2022)
    [Place of publication not identified] : O'Reilly Media, Inc.
    Details
    Language: English
    Pages: 1 online resource (1 video file (59 min.)) , sound, color.
    Edition: [First edition].
    DDC: 005.7
    Keywords: Big data ; Software architecture ; Database management ; Instructional films ; Nonfiction films ; Internet videos
    Abstract: Join us for a special conversation on data mesh with Neal Ford and data mesh creator Zhamak Dehghani. You'll learn how Zhamak's ideas around data mesh have evolved since she first presented the concept in 2018 and what has happened in the space since the publication of her book, Data Mesh: Delivering Data-Driven Value at Scale, in March of 2022. You'll also hear about some of the things that have made data mesh such a paradigm shift and discover ways to combat the potential for its misuse and abuse. This is a chance for you to hear about architecture and Neal and Zhamak's own career journeys. They'll spend a few minutes covering the trends that are influencing architecture, then tell you what you need to know to stay ahead of the curve. What you'll learn and how you can apply it Explore the data mesh paradigm and get tips for building one yourself See what's coming next with software architecture This recording of a live event is for you because... You want to learn about building and maintaining a data mesh. You're looking for ways to improve your architecture. Recommended follow-up: Read Data Mesh (book) Read Software Architecture Metrics (book) Read Designing Data-Intensive Applications (book) Read Software Architecture: The Hard Parts (book) Attend Software Architecture Superstream: Architecture Meets Data (live event on November 30).
    Note: Online resource; title from title details screen (O'Reilly, viewed September 27, 2022)
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  • 67
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    The digital era : customs and practices; 3 (2022)
    Hoboken, NJ : John Wiley & Sons, Incorporated
    Details
    ISBN: 1119469007 , 9781119469001 , 1394165188 , 9781394165186
    Language: English
    Pages: 1 online resource (281 pages)
    Series Statement: Information systems, web and pervasive computing series
    Parallel Title: Erscheint auch als
    DDC: 005.7
    Keywords: Big data ; Artificial intelligence ; Artificial intelligence ; Big data
    Abstract: For 200 years, industry mastered iron, fire, strength and energy. Today, electronics shape our everyday objects, integrating chips everywhere: computers, phones, keys, games, household appliances, etc. Data, software and calculation frame the conduct of men and the administration of things. Everything is translated into data: the figure is king. This third and last volume of the series examines the creative destruction induced by digital, modifying manners and customs, law, society and politics.
    Note: Description based on publisher supplied metadata and other sources
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  • 68
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    Data engineering with Google Cloud Platform : a practical guide to operationalizing scalable data analytics systems on GCP (2022)
    Birmingham : Packt Publishing
    Details
    ISBN: 9781800565067 , 1800565062
    Language: English
    Pages: 1 online resource
    Parallel Title: Erscheint auch als
    DDC: 005.7
    Keywords: Big data ; Cloud computing ; Web services ; Computer organization ; Données volumineuses ; Infonuagique ; Services Web ; Ordinateurs ; Conception et construction ; Electronic books
    Abstract: Build and deploy your own data pipelines on GCP, make key architectural decisions, and gain the confidence to boost your career as a data engineer Key Features Understand data engineering concepts, the role of a data engineer, and the benefits of using GCP for building your solution Learn how to use the various GCP products to ingest, consume, and transform data and orchestrate pipelines Discover tips to prepare for and pass the Professional Data Engineer exam Book Description With this book, you'll understand how the highly scalable Google Cloud Platform (GCP) enables data engineers to create end-to-end data pipelines right from storing and processing data and workflow orchestration to presenting data through visualization dashboards. Starting with a quick overview of the fundamental concepts of data engineering, you'll learn the various responsibilities of a data engineer and how GCP plays a vital role in fulfilling those responsibilities. As you progress through the chapters, you'll be able to leverage GCP products to build a sample data warehouse using Cloud Storage and BigQuery and a data lake using Dataproc. The book gradually takes you through operations such as data ingestion, data cleansing, transformation, and integrating data with other sources. You'll learn how to design IAM for data governance, deploy ML pipelines with the Vertex AI, leverage pre-built GCP models as a service, and visualize data with Google Data Studio to build compelling reports. Finally, you'll find tips on how to boost your career as a data engineer, take the Professional Data Engineer certification exam, and get ready to become an expert in data engineering with GCP. By the end of this data engineering book, you'll have developed the skills to perform core data engineering tasks and build efficient ETL data pipelines with GCP. What you will learn Load data into BigQuery and materialize its output for downstream consumption Build data pipeline orchestration using Cloud Composer Develop Airflow jobs to orchestrate and automate a data warehouse Build a Hadoop data lake, create ephemeral clusters, and run jobs on the Dataproc cluster Leverage Pub/Sub for messaging and ingestion for event-driven systems Use Dataflow to perform ETL on streaming data Unlock the power of your data with Data Studio Calculate the GCP cost estimation for your end-to-end data solutions Who this book is for This book is for data engineers, data analysts, and anyone looking to design and manage data processing pipelines using GCP. You'll find this book useful if you are preparing to take Google's Professional Data Engineer exam. Beginner-level understanding of data science, the Python programming language, and Linux commands is necessary. A basic understanding of data processing and cloud computing, in general, will help you make the most out of this book.
    Note: Print on demand edition. - Description based on CIP data; resource not viewed
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  • 69
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    Bioinformatics and medical applications : big data using deep learning algorithms (2022)
    Hoboken, NJ : Scrivener Publishing
    Details
    ISBN: 9781119792673 , 1119792673 , 9781119792666 , 1119792665 , 9781119791836
    Language: English
    Pages: 1 online resource
    Parallel Title: Erscheint auch als
    DDC: 610.285
    Keywords: Medical informatics ; Bioinformatics ; Big data ; Deep learning (Machine learning) ; Computational Biology ; Médecine ; Informatique ; Bio-informatique ; Données volumineuses ; Electronic books
    Abstract: BIOINFORMATICS AND MEDICAL APPLICATIONS The main topics addressed in this book are big data analytics problems in bioinformatics research such as microarray data analysis, sequence analysis, genomics-based analytics, disease network analysis, techniques for big data analytics, and health information technology. Bioinformatics and Medical Applications: Big Data Using Deep Learning Algorithms analyses massive biological datasets using computational approaches and the latest cutting-edge technologies to capture and interpret biological data. The book delivers various bioinformatics computational methods used to identify diseases at an early stage by assembling cutting-edge resources into a single collection designed to enlighten the reader on topics focusing on computer science, mathematics, and biology. In modern biology and medicine, bioinformatics is critical for data management. This book explains the bioinformatician's important tools and examines how they are used to evaluate biological data and advance disease knowledge. The editors have curated a distinguished group of perceptive and concise chapters that presents the current state of medical treatments and systems and offers emerging solutions for a more personalized approach to healthcare. Applying deep learning techniques for data-driven solutions in health information allows automated analysis whose method can be more advantageous in supporting the problems arising from medical and health-related information. Audience The primary audience for the book includes specialists, researchers, postgraduates, designers, experts, and engineers, who are occupied with biometric research and security-related issues.
    Note: Description based on online resource; title from digital title page (viewed on April 28, 2022)
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    URL: Cover
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  • 70
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    Infrastructure and Ops Hour with Sam Newman : Data mesh with Zhamak Dehghani (2022)
    [Place of publication not identified] : O'Reilly Media, Inc.
    Details
    Language: English
    Pages: 1 online resource (1 video file (58 min.)) , sound, color.
    Edition: [First edition].
    DDC: 005.7
    Keywords: Big data ; Database management ; Information technology Management ; Données volumineuses ; Bases de données ; Gestion ; Technologie de l'information ; Gestion ; Big data ; Database management ; Information technology ; Management ; Instructional films ; Internet videos ; Nonfiction films ; Instructional films ; Nonfiction films ; Internet videos ; Films de formation ; Films autres que de fiction ; Vidéos sur Internet ; Webcast
    Abstract: Our data management solutions no longer match the complexity of organizations, the proliferation of data sources, and the scope of our aspirations to get value from data with AI and analytics. These pain points have led to a new paradigm: the data mesh. Join us for a special conversation on data mesh with Sam Newman and data mesh founder Zhamak Dehghani. They'll discuss how we got to this inflection point with data, then dive into some of the benefits (and challenges) of data mesh--treating data as a product that considers domains as a primary concern and applies platform thinking to create self-serve data infrastructure. This is a chance for you to hear Sam and Zhamak speak about infrastructure and ops and their own career journeys. They'll spend a few minutes covering the trends that are influencing infrastructure, then tell you what you need to know to stay ahead of the curve. What you'll learn and how you can apply it Explore the origins and opportunities of the data mesh approach See what's coming next for infrastructure and ops This recording of a live event is for you because... You want to learn about data mesh, data as a product, and what that means for operations. You're looking for ways to implement data best practices and streamline your value delivery. Recommended follow-up: Read Data Mesh (early release book) Attend Data Mesh in Practice--with Interactivity (live online training course with Max Schultze and Arif Wider) Please note that slides or supplemental materials are not available for download from this recording. Resources are only provided at the time of the live event.
    Note: Online resource; title from title details screen (O'Reilly, viewed April 12, 2022)
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  • 71
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    Big data analytics and machine intelligence in biomedical and health informatics : concepts, methodologies, tools and applications (2022)
    Beverly, MA : John Wiley & Sons
    Details
    ISBN: 9781119792376 , 1119792371 , 9781119792369 , 1119792363 , 9781119791737
    Language: English
    Pages: 1 online resource , illustrations (chiefly color)
    Parallel Title: Erscheint auch als
    DDC: 610.285
    Keywords: Medical informatics ; Big data ; Artificial intelligence ; Artificial intelligence ; Big data ; Medical informatics ; Electronic books
    Abstract: BIG DATA ANALYTICS AND MACHINE INTELLIGENCE IN BIOMEDICAL AND HEALTH INFORMATICS Provides coverage of developments and state-of-the-art methods in the broad and diversified data analytics field and applicable areas such as big data analytics, data mining, and machine intelligence in biomedical and health informatics. The novel applications of Big Data Analytics and machine intelligence in the biomedical and healthcare sector is an emerging field comprising computer science, medicine, biology, natural environmental engineering, and pattern recognition. Biomedical and health informatics is a new era that brings tremendous opportunities and challenges due to the plentifully available biomedical data and the aim is to ensure high-quality and efficient healthcare by analyzing the data. The 12 chapters in??Big Data Analytics and Machine Intelligence in Biomedical and Health Informatics??cover the latest advances and developments in health informatics, data mining, machine learning, and artificial intelligence. They have been organized with respect to the similarity of topics addressed, ranging from issues pertaining to the Internet of Things (IoT) for biomedical engineering and health informatics, computational intelligence for medical data processing, and Internet of Medical Things??(IoMT). New researchers and practitioners working in the field will benefit from reading the book as they can quickly ascertain the best performing methods and compare the different approaches. Audience Researchers and practitioners working in the fields of biomedicine, health informatics, big data analytics, Internet of Things, and machine learning.
    Note: Includes bibliographical references and index. - Online resource; title from digital title page (viewed on June 07, 2022)
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    URL: Inhaltsverzeichnis
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  • 72
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    Cyber resiliency with Splunk Enterprise and IBM FlashSystem Storage Safeguarded Copy with IBM Copy Services Manager (2022)
    [Poughkeepsie, New York] : IBM Redbooks
    Details
    ISBN: 9780738460970 , 0738460974
    Language: English
    Pages: 1 online resource (42 pages) , illustrations
    Edition: [First edition].
    DDC: 658.4/038011
    Keywords: Enterprise resource planning ; Management information systems ; Electronic data processing ; Data mining ; Big data
    Abstract: The focus of this document is to highlight early threat detection by using Splunk Enterprise and proactively start a cyber resilience workflow in response to a cyberattack or malicious user action. The workflow uses IBM® Copy Services Manager (CSM) as orchestration software to invoke the IBM FlashSystem® storage Safeguarded Copy function, which creates an immutable copy of the data in an air-gapped form on the same IBM FlashSystem Storage for isolation and eventual quick recovery. This document explains the steps that are required to enable and forward IBM FlashSystem audit logs and set a Splunk forwarder configuration to forward local event logs to Splunk Enterprise. This document also describes how to create various alerts in Splunk Enterprise to determine a threat, and configure and invoke an appropriate response to the detected threat in Splunk Enterprise. This document explains the lab setup configuration steps that are involved in configuring various components like Splunk Enterprise, Splunk Enterprise config files for custom apps, IBM CSM, and IBM FlashSystem Storage. The last steps in the lab setup section demonstrate the automated Safeguarded Copy creation and validation steps. This document also describes brief steps for configuring various components and integrating them. This document demonstrates a use case for protecting a Microsoft SQL database (DB) volume that is created on IBM FlashSystem Storage. When a threat is detected on the Microsoft SQL DB volume, Safeguarded Copy starts on an IBM FlashSystem Storage volume. The Safeguarded Copy creates an immutable copy of the data, and the same data volume can be recovered or restored by using IBM CSM. This publication does not describe the installation procedures for Splunk Enterprise, Splunk Forwarder for IBM CSM, th Microsoft SQL server, or the IBM FlashSystem Storage setup. It is assumed that the reader of the book has a basic understanding of system, Windows, and DB administration; storage administration; and has access to the required software and documentation that is used in this document.
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  • 73
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    Cloud computing enabled big-data analytics in wireless ad-hoc networks (2022)
    Boca Raton, FL ; : CRC Press
    Details
    ISBN: 9781000539493 , 1000539490
    Language: English
    Pages: 1 online resource , color illustrations
    Edition: First edition.
    Series Statement: Wireless networks and mobile communications. Classification, advancement and applications
    DDC: 004.67/8
    Keywords: Ad hoc networks (Computer networks) ; Cloud computing ; Big data ; Réseaux ad hoc (Réseaux d'ordinateurs) ; Infonuagique ; Données volumineuses ; Ad hoc networks (Computer networks) ; Big data ; Cloud computing
    Abstract: "This reference text covers intelligent computing through Internet of Things (IoT) and Big Data in Vehicular Environment in a single volume. The text covers important topics including topology-based routing protocols, heterogeneous wireless networks, security risks, software-defined vehicular Ad-hoc network, vehicular delay tolerant networks, and energy harvesting for WSNs using rectenna"--
    Note: Includes bibliographical references and index. - Online resource; title from PDF title page (EBSCO, viewed April 4, 2022)
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  • 74
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    Learn Hadoop and Azure HDInsight basics this evening (in 2 hours) (2022)
    [Place of publication not identified] : Packt Publishing
    Details
    Language: English
    Pages: 1 online resource (1 video file (2 hr., 27 min.)) , sound, color.
    Edition: [First edition].
    DDC: 006.3/12
    Keywords: Apache Hadoop ; Windows Azure ; Big data ; Data mining ; Data Mining ; Données volumineuses ; Exploration de données (Informatique) ; Instructional films ; Nonfiction films ; Internet videos ; Films de formation ; Films autres que de fiction ; Vidéos sur Internet ; Webcast
    Abstract: Be a part of the hands-on course for beginners to master the basics of the Hadoop Ecosystem. Take a tour of the concepts and fundamental understandings of Hadoop and implement its components in Azure through HDInsight. About This Video A short, hands-on yet comprehensive course on getting started with Hadoop and Azure HDInsight This module will prepare you to start learning big data in Azure Cloud using HDInsight All the requisite resource files come bundled with this course In Detail The Apache Hadoop is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. As numerous organizations move to big data, the demand for Hadoop professionals has been increasing massively. Thus, Hadoop is one of the most valuable skills to learn in today's world. In this course, you will start with learning a fundamental of the Hadoop Ecosystem and 3 main building blocks. Then, you will look at the challenges with Hadoop and how HDInsight solves these challenges. You will also learn cluster types, HDInsight architecture, and other important aspects of Azure HDInsight. After that, you will fetch data from the data lake, process it through Hive, and later will store data in SQL Server. Finally, you will see how HDInsight makes Hadoop easy, and go through a simple demo where you will fetch data from the data lake, process it through Hive, and later will store data in SQL Server. By the end of this course, you will have a solid understanding of the challenges that come with big data and how to solve them using Hadoop and HDInsight. You will be able to extract data from the data lake and process it through Hive and later store data in a SQL server. Audience This course is for beginners in Microsoft Azure Platform or anyone who is looking forward to starting their career as an Azure data engineer. If you are a Microsoft Azure data engineer, data scientist, database and BI developer, database administrator, data analyst, or similar profile, then this course will help you further in your professional venture. On-premises database-related profiles who want to learn how to implement Hadoop components in Azure Cloud can opt for this course. If you know basic T-SQL and database concepts, this course will be much easier to understand.
    Note: "Updated in March 2022.". - Online resource; title from title details screen (O'Reilly, viewed March 30, 2022)
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  • 75
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    Asynchronous geographic logical volume mirroring best practices for cloud deployment (2022)
    [Place of publication not identified] : IBM Redbooks
    Details
    ISBN: 9780738460468 , 073846046X
    Language: English
    Pages: 1 online resource (72 pages) , illustrations
    Edition: First edition.
    Series Statement: IBM Redbooks
    DDC: 004.67/82
    Keywords: Cloud computing ; Computer architecture ; Big data ; Computing platforms ; Infonuagique ; Ordinateurs ; Architecture ; Données volumineuses ; Plateformes (Informatique) ; Big data ; Cloud computing ; Computer architecture ; Computing platforms ; Electronic books
    Abstract: This IBM® Redpaper™ publication describes IBM Geographic Logical Volume Manager (GLVM) for data mirroring in cloud deployments. Asynchronous GLVM provides IBM AIX® based mirroring of data across distance over networks. It is highly recommended that Asynchronous GLVM be deployed with PowerHA SystemMirror for AIX Enterprise Edition. PowerHA® SystemMirror® provides robust workload stack HA management, handles many errors in the environment, and helps recover Asynchronous GLVM better. PowerHA SystemMirror also provides interfaces for easy setup of Asynchronous GLVM and disk management. This IBM Redpaper publication provides guidelines in relation to GLVM deployments for private or public clouds. This publication is intended to help with the requirements to configure and implement GLVM for cloud configurations. This paper addresses topics for IT architects, IT specialists, sellers and anyone who wants to implement and manage high availability (HA) and Disaster Recovery (DR) in the cloud. The publication also provides documentation to transfer the how-to skills to the technical teams, and solution guidance to the sales team. This paper compliments the documentation that is available at the IBM Documentation web page and aligns with the educational materials that are provided by IBM Systems Technical Education.
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  • 76
    Online Resource
    Online Resource
    Advanced analytics and deep learning models (2022)
    Hoboken, NJ : John Wiley & Sons
    Details
    ISBN: 9781119792437 , 1119792436 , 9781119792413 , 111979241X , 9781119791751
    Language: English
    Pages: 1 online resource
    Parallel Title: Erscheint auch als
    DDC: 006.3/1
    Keywords: Deep learning (Machine learning) ; Artificial intelligence ; Big data ; Artificial intelligence ; Big data ; Deep learning (Machine learning) ; Electronic books
    Abstract: Advanced Analytics and Deep Learning Models The book provides readers with an in-depth understanding of concepts and technologies related to the importance of analytics and deep learning in many useful real-world applications such as e-healthcare, transportation, agriculture, stock market, etc. Advanced analytics is a mixture of machine learning, artificial intelligence, graphs, text mining, data mining, semantic analysis. It is an approach to data analysis. Beyond the traditional business intelligence, it is a semi and autonomous analysis of data by using different techniques and tools. However, deep learning and data analysis both are high centers of data science. Almost all the private and public organizations collect heavy amounts of data, i.e., domain-specific data. Many small/large companies are exploring large amounts of data for existing and future technology. Deep learning is also exploring large amounts of unsupervised data making it beneficial and effective for big data. Deep learning can be used to deal with all kinds of problems and challenges that include collecting unlabeled and uncategorized raw data, extracting complex patterns from a large amount of data, retrieving fast information, tagging data, etc. This book contains 16 chapters on artificial intelligence, machine learning, deep learning, and their uses in many useful sectors like stock market prediction, a recommendation system for better service selection, e-healthcare, telemedicine, transportation. There are also chapters on innovations and future opportunities with fog computing/cloud computing and artificial intelligence. Audience Researchers in artificial intelligence, big data, computer science, and electronic engineering, as well as industry engineers in healthcare, telemedicine, transportation, and the financial sector. The book will also be a great source for software engineers and advanced students who are beginners in the field of advanced analytics in deep learning.
    Note: Includes bibliographical references and index. - Online resource; title from digital title page (viewed on May 11, 2022)
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  • 77
    Online Resource
    Online Resource
    Sustainability, big data, and corporate social responsibility : evidence from the tourism industry (2022)
    Boca Raton : CRC Press
    Details
    ISBN: 9781003138051 , 1003138055 , 9781000572414 , 1000572412 , 9781000572469 , 1000572463
    Language: English
    Pages: 1 online resource (xiii, 263 pages)
    Edition: First edition.
    Series Statement: Information Technology, Management and Operations Research Practices
    Parallel Title: Erscheint auch als
    Keywords: Social responsibility of business ; Big data ; Entreprises ; Responsabilité sociale ; Données volumineuses ; TECHNOLOGY / Operations Research ; COMPUTERS / Database Management / Data Mining ; Big data ; Social responsibility of business
    Abstract: "This book aims to provide theoretical and empirical frameworks and highlights the challenges and solutions with using Big Data for Corporate Social Responsibility (CSR) and Sustainability in the field of digital transformation and tourism. Sustainability, Big Data, and Corporate Social Responsibility: Evidence from the Tourism Industry offers a theoretical and empirical framework in the field of digital transformation and applies it to the tourism sector. It discusses Big Data used with CSR and sustainability for the improvement of innovation and highlights the challenges and prospects. It presents a modern insight and approach for use by decision-makers as an application to solve various problems and explores how data collection can shed light on consumer behavior making it possible to account for existing situations and plan for the future. This book is intended to provide a modern insight for researcher, students, professionals, and decision-makers on the application of Big Data to improve CSR and sustainability in the tourism sector"--
    Note: Includes bibliographical references and index. - Description based on online resource; title from digital title page (viewed on May 10, 2022)
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  • 78
    Online Resource
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    The Kaggle book : data analysis and machine learning for competitive data science (2022)
    [Birmingham] : Packt Publishing
    Details
    ISBN: 9781804612361 , 1804612367
    Language: English
    Pages: 1 online resource (1 audio file (12 hr., 29 min.))
    Edition: [First edition].
    Series Statement: Expert insight
    DDC: 006.3/1
    Keywords: Machine learning ; Big data ; Downloadable audio books ; Audiobooks
    Abstract: Get a step ahead of your competitors with insights from over 30 Kaggle Masters and Grandmasters. Discover tips, tricks, and best practices for competing effectively on Kaggle and becoming a better data scientist. About This Audiobook Learn how Kaggle works and how to make the most of competitions from over 30 expert Kagglers Sharpen your modeling skills with ensembling, feature engineering, adversarial validation and AutoML A concise collection of smart data handling techniques for modeling and parameter tuning In Detail Millions of data enthusiasts from around the world compete on Kaggle, the most famous data science competition platform of them all. Participating in Kaggle competitions is a surefire way to improve your data analysis skills, network with an amazing community of data scientists, and gain valuable experience to help grow your career. The first audiobook of its kind, The Kaggle Book assembles in one place the techniques and skills you'll need for success in competitions, data science projects, and beyond. Two Kaggle Grandmasters walk you through modeling strategies you won't easily find elsewhere, and the knowledge they've accumulated along the way. As well as Kaggle-specific tips, you'll learn more general techniques for approaching tasks based on image, tabular, textual data, and reinforcement learning. You'll design better validation schemes and work more comfortably with different evaluation metrics. Whether you want to climb the ranks of Kaggle, build some more data science skills, or improve the accuracy of your existing models, this audiobook is for you. Audience This audiobook is suitable for anyone new to Kaggle, veteran users, and anyone in between. Data analysts/scientists who are trying to do better in Kaggle competitions and secure jobs with tech giants will find this audiobook useful. A basic understanding of machine learning concepts will help you make the most of this audiobook.
    Note: Online resource; title from title details screen (O'Reilly, viewed September 20, 2022)
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  • 79
    Online Resource
    Online Resource
    Implementing a modern data catalog to power data intelligence : make trustworthy data central to your organization (2022)
    [Sebastopol, California] : O'Reilly Media, Inc.
    Details
    Language: English
    Pages: 1 online resource (38 pages)
    Edition: [First edition].
    DDC: 005.74
    Keywords: Metadata ; Information organization ; Database management ; Big data ; Machine learning ; Electronic books
    Abstract: Are you looking to use data as a strategic asset in your organization, so that more people can make better, data-driven decisions and accelerate time to value? This report explains how. Whether you're working on self-service analytics, data governance, or cloud data migration, authors Fadi Maali, an experienced data engineer and the lead editor of the DCAT Specification, and Jason Lim, director of product and cloud marketing at Alation, show you why a data catalog is the starting point and center of all of it. Modern data catalogs are collections of metadata describing data assets and their usage. They provide relevant functionality to support metadata management, enrichment, and search. Not only do these catalogs help you find relevant data, they also guide you through the data's proper use. This report shows you how a data catalog can help you easily find and then use the data you need.
    Note: Includes bibliographical references
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  • 80
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    Simplifying Data Engineering and Analytics with Delta : Create Analytics-Ready Data That Fuels Artificial Intelligence and Business Intelligence (2022)
    Birmingham : Packt Publishing, Limited
    Details
    ISBN: 9781801810715 , 1801810710
    Language: English
    Pages: 1 online resource (335 pages)
    DDC: 006.3/12
    Keywords: Database management ; Data mining ; Big data ; Business Data processing ; Electronic books
    Abstract: Explore how Delta brings reliability, performance, and governance to your data lake and all the AI and BI use cases built on top of it Key Features Learn Delta's core concepts and features as well as what makes it a perfect match for data engineering and analysis Solve business challenges of different industry verticals using a scenario-based approach Make optimal choices by understanding the various tradeoffs provided by Delta Book Description Delta helps you generate reliable insights at scale and simplifies architecture around data pipelines, allowing you to focus primarily on refining the use cases being worked on. This is especially important when you consider that existing architecture is frequently reused for new use cases. In this book, you'll learn about the principles of distributed computing, data modeling techniques, and big data design patterns and templates that help solve end-to-end data flow problems for common scenarios and are reusable across use cases and industry verticals. You'll also learn how to recover from errors and the best practices around handling structured, semi-structured, and unstructured data using Delta. After that, you'll get to grips with features such as ACID transactions on big data, disciplined schema evolution, time travel to help rewind a dataset to a different time or version, and unified batch and streaming capabilities that will help you build agile and robust data products. By the end of this Delta book, you'll be able to use Delta as the foundational block for creating analytics-ready data that fuels all AI/BI use cases. What you will learn Explore the key challenges of traditional data lakes Appreciate the unique features of Delta that come out of the box Address reliability, performance, and governance concerns using Delta Analyze the open data format for an extensible and pluggable architecture Handle multiple use cases to support BI, AI, streaming, and data discovery Discover how common data and machine learning design patterns are executed on Delta Build and deploy data and machine learning pipelines at scale using Delta Who this book is for Data engineers, data scientists, ML practitioners, BI analysts, or anyone in the data domain working with big data will be able to put their knowledge to work with this practical guide to executing pipelines and supporting diverse use cases using the Delta protocol. Basic knowledge of SQL, Python programming, and Spark is required to get the most out of this book.
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  • 81
    Online Resource
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    The economics of data, analytics, and digital transformation (2022)
    [Birmingham, UK] : Packt Publishing
    Details
    ISBN: 9781804613153 , 1804613150
    Language: English
    Pages: 1 online resource (1 audio file (03 hr., 37 min.))
    Edition: [First edition].
    DDC: 658.4038
    Keywords: Information technology Management ; Big data ; Audiobooks
    Abstract: Build a continuously learning and adapting organization that can extract increasing levels of business, customer and operational value from the amalgamation of data and advanced analytics such as AI and Machine Learning About This Audiobook Master the Big Data Business Model Maturity Index methodology to transition to a value-driven organizational mindset Acquire implementable knowledge on digital transformation through 8 practical laws Explore the economics behind digital assets (data and analytics) that appreciate in value when constructed and deployed correctly In Detail In today's digital era, every organization has data, but just possessing enormous amounts of data is not a sufficient market discriminator. The Economics of Data, Analytics, and Digital Transformation aims to provide actionable insights into the real market discriminators, including an organization's data-fueled analytics products that inspire innovation, deliver insights, help make practical decisions, generate value, and produce mission success for the enterprise. The book begins by first building your mindset to be value-driven and introducing the Big Data Business Model Maturity Index, its maturity index phases, and how to navigate the index. You will explore value engineering, where you will learn how to identify key business initiatives, stakeholders, advanced analytics, data sources, and instrumentation strategies that are essential to data science success. The book will help you accelerate and optimize your company's operations through AI and machine learning. By the end of the book, you will have the tools and techniques to drive your organization's digital transformation. Here are a few words from Dr. Kirk Borne, Data Scientist and Executive Advisor at Booz Allen Hamilton, about the book: "Data analytics should first and foremost be about action and value. Consequently, the great value of this book is that it seeks to be actionable. It offers a dynamic progression of purpose-driven ignition points that you can act upon." Audience This book is designed to benefit everyone from students who aspire to study the economic fundamentals behind data and digital transformation to established business leaders and professionals who want to learn how to leverage data and analytics to accelerate their business careers
    Note: Online resource; title from title details screen (O'Reilly, viewed October 4, 2022)
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  • 82
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    50 hours of big data, PySpark, AWS, Scala, and Scraping (2022)
    [Place of publication not identified] : Packt Publishing
    Details
    ISBN: 9781803237039 , 1803237031
    Language: English
    Pages: 1 online resource (1 video file (54 hr., 36 min.)) , sound, color.
    Edition: [First edition].
    DDC: 005.1
    Keywords: Data mining ; Big data ; Instructional films ; Nonfiction films ; Internet videos
    Abstract: Learn, build, and execute big data strategies with Scala and Spark, PySpark and AWS, data scraping and data mining with Python, and master MongoDB About This Video Data scraping and data mining for beginners to pro with Python Clear unfolding of concepts with examples in Python, Scrapy, Scala, PySpark, and MongoDB Master Big Data with PySpark and AWS In Detail Part 1 is designed to reflect the most in-demand Scala skills. It provides an in-depth understanding of core Scala concepts. We will wrap up with a discussion on Map Reduce and ETL pipelines using Spark from AWS S3 to AWS RDS (includes six mini-projects and one Scala Spark project). Part 2 covers PySpark to perform data analysis. You will explore Spark RDDs, Dataframes, a bit of Spark SQL queries, transformations, and actions that can be performed on the data using Spark RDDs and dataframes, the ecosystem of Spark and Hadoop, and their underlying architecture. You will also learn how we can leverage AWS storage, databases, computations, and how Spark can communicate with different AWS services. Part 3 is all about data scraping and data mining. You will cover important concepts such as Internet Browser execution and communication with the server, synchronous and asynchronous, parsing data in response from the server, tools for data scraping, Python requests module, and more. In Part 4, you will be using MongoDB to develop an understanding of the NoSQL databases. You will explore the basic operations and explore the MongoDB query, project and update operators. We will wind up this section with two projects: Developing a CRUD-based application using Django and MongoDB and implementing an ETL pipeline using PySpark to dump the data in MongoDB. By the end of this course, you will be able to relate the concepts and practical aspects of learned technologies with real-world problems. Audience This course is designed for absolute beginners who want to create intelligent solutions, study with actual data, and enjoy learning theory and then putting it into practice. Data scientists, machine learning experts, and drop shippers will all benefit from this training. A basic understanding of programming, HTML tags, Python, SQL, and Node JS is required. However, no prior knowledge of data scraping, and Scala is needed.
    Note: "Updated in March 2022.". - "AI Sciences.". - Online resource; title from title details screen (O'Reilly, viewed May 10, 2022)
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  • 83
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    In-Memory Analytics with Apache Arrow : Perform Fast and Efficient Data Analytics on Both Flat and Hierarchical Structured Data (2022)
    Birmingham : Packt Publishing, Limited
    Details
    ISBN: 9781801073431 , 1801073430
    Language: English
    Pages: 1 online resource (392 p.)
    DDC: 006.3/12
    Keywords: Data mining ; Big data ; Electronic books
    Abstract: Process tabular data and build high-performance query engines on modern CPUs and GPUs using Apache Arrow, a standardized language-independent memory format, for optimal performance Key Features Learn about Apache Arrow's data types and interoperability with pandas and Parquet Work with Apache Arrow Flight RPC, Compute, and Dataset APIs to produce and consume tabular data Reviewed, contributed, and supported by Dremio, the co-creator of Apache Arrow Book Description Apache Arrow is designed to accelerate analytics and allow the exchange of data across big data systems easily. In-Memory Analytics with Apache Arrow begins with a quick overview of the Apache Arrow format, before moving on to helping you to understand Arrow's versatility and benefits as you walk through a variety of real-world use cases. You'll cover key tasks such as enhancing data science workflows with Arrow, using Arrow and Apache Parquet with Apache Spark and Jupyter for better performance and hassle-free data translation, as well as working with Perspective, an open source interactive graphical and tabular analysis tool for browsers. As you advance, you'll explore the different data interchange and storage formats and become well-versed with the relationships between Arrow, Parquet, Feather, Protobuf, Flatbuffers, JSON, and CSV. In addition to understanding the basic structure of the Arrow Flight and Flight SQL protocols, you'll learn about Dremio's usage of Apache Arrow to enhance SQL analytics and discover how Arrow can be used in web-based browser apps. Finally, you'll get to grips with the upcoming features of Arrow to help you stay ahead of the curve. By the end of this book, you will have all the building blocks to create useful, efficient, and powerful analytical services and utilities with Apache Arrow. What you will learn Use Apache Arrow libraries to access data files both locally and in the cloud Understand the zero-copy elements of the Apache Arrow format Improve read performance by memory-mapping files with Apache Arrow Produce or consume Apache Arrow data efficiently using a C API Use the Apache Arrow Compute APIs to perform complex operations Create Arrow Flight servers and clients for transferring data quickly Build the Arrow libraries locally and contribute back to the community Who this book is for This book is for developers, data analysts, and data scientists looking to explore the capabilities of Apache Arrow from the ground up. This book will also be useful for any engineers who are working on building utilities for data analytics and query engines, or otherwise working with tabular data, regardless of the programming language. Some familiarity with basic concepts of data analysis will help you to get the most out of this book but isn't required. Code examples are provided in the C++, Go, and Python programming languages.
    Note: Description based upon print version of record
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  • 84
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    The ultimate guide to building a Google Cloud foundation : a one-on-one tutorial with one of Google's top trainers (2022)
    Birmingham : Packt Publishing, Limited
    Details
    ISBN: 9781803231990 , 1803231998
    Language: English
    Pages: 1 online resource (284 pages)
    DDC: 005.7
    Keywords: Big data ; Cloud computing ; Web services ; Big data ; Cloud computing ; Web services ; Electronic books
    Abstract: Follow Google's own ten-step plan to construct a secure, reliable, and extensible foundation for all your Google Cloud base infrastructural needs Key Features Build your foundation in Google Cloud with this clearly laid out, step-by-step guide Get expert advice from one of Google's top trainers Learn to build flexibility and security into your Google Cloud presence from the ground up Book Description From data ingestion and storage, through data processing and data analytics, to application hosting and even machine learning, whatever your IT infrastructural need, there's a good chance that Google Cloud has a service that can help. But instant, self-serve access to a virtually limitless pool of IT resources has its drawbacks. More and more organizations are running into cost overruns, security problems, and simple "why is this not working?" headaches. This book has been written by one of Google's top trainers as a tutorial on how to create your infrastructural foundation in Google Cloud the right way. By following Google's ten-step checklist and Google's security blueprint, you will learn how to set up your initial identity provider and create an organization. Further on, you will configure your users and groups, enable administrative access, and set up billing. Next, you will create a resource hierarchy, configure and control access, and enable a cloud network. Later chapters will guide you through configuring monitoring and logging, adding additional security measures, and enabling a support plan with Google. By the end of this book, you will have an understanding of what it takes to leverage Terraform for properly building a Google Cloud foundational layer that engenders security, flexibility, and extensibility from the ground up. What you will learn Create an organizational resource hierarchy in Google Cloud Configure user access, permissions, and key Google Cloud Platform (GCP) security groups Construct well thought out, scalable, and secure virtual networks Stay informed about the latest logging and monitoring best practices Leverage Terraform infrastructure as code automation to eliminate toil Limit access with IAM policy bindings and organizational policies Implement Google's secure foundation blueprint Who this book is for This book is for anyone looking to implement a secure foundational layer in Google Cloud, including cloud engineers, DevOps engineers, cloud security practitioners, developers, infrastructural management personnel, and other technical leads. A basic understanding of what the cloud is and how it works, as well as a strong desire to build out Google Cloud infrastructure the right way will help you make the most of this book. Knowledge of working in the terminal window from the command line will be beneficial
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  • 85
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    How AI is improving data management : aritificial intelligence is quietly improving the management of data, including its quality, accessibility, and security (2022)
    [Cambridge, Massachusetts] : MIT Sloan Management Review
    Details
    Language: English
    Pages: 1 online resource (7 pages)
    Edition: [First edition].
    DDC: 006.3
    Keywords: Artificial intelligence Data processing ; Big data
    Abstract: Artificial intelligence has been applied successfully in thousands of ways, but one of the less visible and less dramatic ones is improving data management. The authors describe five common areas of data management -- classifying, cataloging, quality, security, and data integration -- where they see AI playing important roles. They also discuss the vendor landscape and the ways that humans are essential to data management.
    Note: "Reprint 64309."
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  • 86
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    ENTERPRISE ARCHITECTURE FOR DIGITAL BUSINESS : transforming IT (2022)
    [Erscheinungsort nicht ermittelbar] : O'REILLY MEDIA
    Details
    ISBN: 9781098121426 , 1098121422
    Language: English
    Pages: 1 online resource
    Parallel Title: Erscheint auch als
    DDC: 658/.05
    Keywords: Organizational change Technological innovations ; Business planning Data processing ; Big data
    Abstract: Digital transformation has accelerated nearly tenfold in recent years as both a business and technology journey. Yet, most white papers and how-to guides still focus solely on the business side, rather than include methods for optimizing the technology behind it. This handbook shows CIOs, IT directors, and architects how to balance these two concerns successfully. You'll explore current technology trends and shifts required to build a digital business, including how enterprise architecture should evolve if it's to sustain and grow your business. A CIO who can handle digital transformation along with business interests is a rare find. This is the ideal guide to modernizing IT. You'll examine: The latest trends and technologies driving the need for a digital enterprise architecture New components, layers, and concepts that comprise a framework for digital enterprise architecture Skills and technologies you need to modernize an enterprise architecture for a digital business Domains and characteristics of a digital enterprise architecture How to map digital enterprise technologies to the appropriate teams.
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  • 87
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    INTELLIGENT AUTOMATION WITH IBM CLOUD PAK FOR BUSINESS AUTOMATION : a practical guide to automating enterprise business workflows to deliver intelligent solutions (2022)
    [Erscheinungsort nicht ermittelbar] : PACKT PUBLISHING LIMITED
    Details
    ISBN: 9781801811125 , 1801811121 , 9781801814775
    Language: English
    Pages: 1 online resource
    Parallel Title: Erscheint auch als
    DDC: 004.67/82
    Keywords: Cloud computing ; Big data ; Business Data processing ; Information technology Management ; Big data ; Business ; Data processing ; Cloud computing ; Information technology ; Management
    Abstract: Leverage the low-code/no-code approach in IBM Cloud Pak for business automation to accelerate your organization's digital transformation Purchase of the print or Kindle book includes a free eBook PDF Key Features Get a comprehensive understanding of IBM Cloud Pak for Business Automation Take a deep dive into insights on RPA, workflow automation, and automated decisions Deploy and manage production-grade automated solutions for scalability, stability, and performance Book Description COVID-19 has made many businesses change how they work, change how they engage their customers, and even change their products. Several of these businesses have also recognized the need to make these changes within days as opposed to months or weeks. This has resulted in an unprecedented pace of digital transformation; and success, in many cases, depends on how quickly an organization can react to real-time decisions. This book begins by introducing you to IBM Cloud Pak for Business Automation, providing a hands-on approach to project implementation. As you progress through the chapters, you'll learn to take on business problems and identify the relevant technology and starting point. Next, you'll find out how to engage both the business and IT community to better understand business problems, as well as explore practical ways to start implementing your first automation project. In addition, the book will show you how to create task automation, interactive chatbots, workflow automation, and document processing. Finally, you'll discover deployment best practices that'll help you support highly available and resilient solutions. By the end of this book, you'll have a firm grasp on the types of business problems that can be solved with IBM Cloud Pak for Business Automation. What you will learn Understand key IBM automation technologies and learn how to apply them Cover the end-to-end journey of creating an automation solution from concept to deployment Understand the features and capabilities of workflow, decisions, RPA, business applications, and document processing with AI Analyze your business processes and discover automation opportunities with process mining Set up content management solutions that meet business, regulatory, and compliance needs Understand deployment environments supported by IBM Cloud Pak for Business Automation Who this book is for This book is for robotic process automation (RPA) professionals and automation consultants who want to accelerate the digital transformation of their businesses using IBM automation. This book is also useful for solutions architects or enterprise architects looking for best practices to build resilient and scalable AI-driven automation solutions. A basic understanding of business processes, low-code visual modeling techniques, RPA, and AI concepts is assumed.
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  • 88
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    Make your data speak : creating actionable data through Excel for non-technical professionals (2022)
    New York : Apress
    Details
    ISBN: 9781484289426 , 1484289420
    Language: English
    Pages: 1 online resource (261 pages) , illustrations (black and white, and colour).
    Parallel Title: Erscheint auch als
    Keywords: Microsoft Excel (Computer file) ; Data structures (Computer science) ; Database management ; Big data ; Sampling (Statistics)
    Abstract: Gather and analyze data successfully, identify trends, and then create overarching strategies and actionable next steps - all through Excel. This book will show even those who lack a technical background how to make advanced interactive reports with only Excel at hand. Advanced visualization is available to everyone, and this step-by-step guide will show you how. The information in this book is presented in an accessible and understandable way for everyone, regardless of the level of technical skills and proficiency in MS Excel. The dashboard development process is given in the format of step-by-step instructions, taking you through each step in detail. Universal checklists and recommendations of a practicing business analyst and trainer will help in solving various tasks when working with data visualization. Illustrations will help you perceive information easily and quickly. Make Your Data Speak will show you how to master the main rules, techniques and tricks of professional data visualization in just a few days. You will: See how interactive dashboards can be useful for a business Review basic rules for building dashboards Understand why it's important to pay attention to colors and fonts when developing a dashboard Create interactive management reports in Excel .
    Note: Includes index. - Print version record
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  • 89
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    Ji qi xue xi she ji mo shi = : Machine learning design patterns (2022)
    Nanjing : Dong nan da xue chu ban she = Southeast University Press
    Details
    Orig.schr. Ausgabe: 第1版.
    Title: 机器学习设计模式 = : Machine learning design patterns /
    Publisher: 东南大学出版社 = Southeast University Press,
    ISBN: 9787564196776 , 7564196777
    Language: Chinese
    Pages: 1 online resource (386 pages) , illustrations
    Edition: Di 1 ban.
    Uniform Title: Machine learning design patterns
    DDC: 006.31
    Keywords: Machine learning ; Big data ; Big data ; Machine learning
    Abstract: Detailed summary in vernacular field.
    Note: 880-04;O'Reilly Media, Inc. shou quan chu ban
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  • 90
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    Data Mesh in action (2022)
    [Place of publication not identified] : Manning Publications
    Details
    Language: English
    Pages: 1 online resource (1 video file (9 hr., 44 min.)) , sound, color.
    Edition: Video edition.
    DDC: 658.4/038011
    Keywords: Management information systems ; Business Data processing ; Big data ; Instructional films ; Nonfiction films ; Internet videos
    Abstract: Revolutionize the way your organization approaches data with a data mesh! This new decentralized architecture outpaces monolithic lakes and warehouses and can work for a company of any size. In Data Mesh in Action you will learn how to: Implement a data mesh in your organization Turn data into a data product Move from your current data architecture to a data mesh Identify data domains, and decompose an organization into smaller, manageable domains Set up the central governance and local governance levels over data Balance responsibilities between the two levels of governance Establish a platform that allows efficient connection of distributed data products and automated governance Data Mesh in Action reveals how this groundbreaking architecture looks for both startups and large enterprises. You won't need any new technology--this book shows you how to start implementing a data mesh with flexible processes and organizational change. You'll explore both an extended case study and real-world examples. As you go, you'll be expertly guided through discussions around Socio-Technical Architecture and Domain-Driven Design with the goal of building a sleek data-as-a-product system. Plus, dozens of workshop techniques for both in-person and remote meetings help you onboard colleagues and drive a successful transition. About the Technology Business increasingly relies on efficiently storing and accessing large volumes of data. The data mesh is a new way to decentralize data management that radically improves security and discoverability. A well-designed data mesh simplifies self-service data consumption and reduces the bottlenecks created by monolithic data architectures. About the Book Data Mesh in Action teaches you pragmatic ways to decentralize your data and organize it into an effective data mesh. You'll start by building a minimum viable data product, which you'll expand into a self-service data platform, chapter-by-chapter. You'll love the book's unique "sliders" that adjust the mesh to meet your specific needs. You'll also learn processes and leadership techniques that will change the way you and your colleagues think about data. What's Inside Decompose an organization into manageable domains Turn data into a data product Set up central and local governance levels Build a fit-for-purpose data platform Improve management, initiation, and support techniques About the Reader For data professionals. Requires no specific programming stack or data platform. About the Authors Jacek Majchrzak is a hands-on lead data architect. Dr. Sven Balnojan manages data products and teams. Dr. Marian Siwiak is a data scientist and a management consultant for IT, scientific, and technical projects. Quotes This book teleports you into the seat of the chief architect on a data mesh project. - From the Foreword by Jean-Georges Perrin, PayPal A must-read for anyone who works in data. - Prukalpa Sankar, Co-Founder of Atlan Satisfies all those 'what', 'why', and 'how' questions. A unique blend of process and technology, and an excellent, example-driven resource. - Shiroshica Kulatilake, WSO2 The starting point for your journey in the new generation of data platforms. - Arnaud Castelltort, University of Montpellier.
    Note: Online resource; title from title details screen (O'Reilly, viewed October 11, 2023)
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  • 91
    Online Resource
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    Gao xing neng Spark = : High performance Spark / = High performance Spark (2022)
    Beijing : Zhongguo dian li chu ban she = China Electric Power Press Ltd.
    Details
    Orig.schr. Ausgabe: 第一版.
    Title: 高性能 Spark = : High performance Spark /
    Publisher: 北京 : 中国电力出版社 = China Electric Power Press Ltd.
    ISBN: 9787519863531 , 7519863530
    Language: Chinese
    Pages: 1 online resource (371 pages) , illustrations
    Edition: Di yi ban.
    Uniform Title: High performance Spark
    DDC: 006.3/12
    Keywords: Spark (Electronic resource : Apache Software Foundation) ; Big data ; Data mining Computer programs ; Données volumineuses ; Exploration de données (Informatique) ; Logiciels
    Abstract: Detailed summary in vernacular field.
    Note: 880-04;O'Reilly Media, Inc. shou quan chu ban
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  • 92
    Online Resource
    Online Resource
    Python yu ce fen xi shi zhan = : Hands-on predictive analytics with Python / = Hands-on predictive analytics with Python (2022)
    Beijing : Ren min you dian chu ban she = Posts & Telecom Press
    Details
    Orig.schr. Ausgabe: 第1版.
    Title: Python预测分析实战 = : Hands-on predictive analytics with Python /
    Publisher: 北京 : 人民邮电出版社 = Posts & Telecom Press
    ISBN: 9781835463772
    Language: Chinese
    Pages: 1 online resource (256 pages) , illustrations.
    Edition: Di 1 ban.
    Series Statement: Yi bu tu shu
    Uniform Title: Hands-on predictive analytics with Python
    DDC: 005.13/3
    Keywords: Python (Computer program language) ; Application software Development ; Decision making Data processing ; Data mining ; Big data ; Python (Langage de programmation) ; Logiciels d'application ; Développement ; Prise de décision ; Informatique ; Exploration de données (Informatique) ; Données volumineuses
    Abstract: Detailed summary in vernacular field.
    Note: 880-05;Ben shu you Yingguo Packt Publishing gong si shou quan Ren min you dian chu ban she chu ban
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  • 93
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    Online Resource
    Software architecture hour with Neal Ford : Building a data mesh with Zhamak Dehghani (2022)
    [Place of publication not identified] : O'Reilly Media, Inc.
    Details
    Language: English
    Pages: 1 online resource (1 audio file (58 min.))
    Edition: [First edition].
    DDC: 005.7
    Keywords: Big data ; Software architecture ; Database management ; Audiobooks
    Abstract: Join us for a special conversation on data mesh with Neal Ford and data mesh creator Zhamak Dehghani. You'll learn how Zhamak's ideas around data mesh have evolved since she first presented the concept in 2018 and what has happened in the space since the publication of her book, Data Mesh: Delivering Data-Driven Value at Scale, in March of 2022. You'll also hear about some of the things that have made data mesh such a paradigm shift and discover ways to combat the potential for its misuse and abuse. This is a chance for you to hear about architecture and Neal and Zhamak's own career journeys. They'll spend a few minutes covering the trends that are influencing architecture, then tell you what you need to know to stay ahead of the curve. What you'll learn and how you can apply it Explore the data mesh paradigm and get tips for building one yourself See what's coming next with software architecture This recording of a live event is for you because ... You want to learn about building and maintaining a data mesh. You're looking for ways to improve your architecture. Recommended follow-up: Read Data Mesh (book) Read Software Architecture Metrics (book) Read Designing Data-Intensive Applications (book) Read Software Architecture: The Hard Parts (book) Attend Software Architecture Superstream: Architecture Meets Data (live event on November 30).
    Note: Online resource; title from title details screen (O'Reilly, viewed October 25, 2022)
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  • 94
    Online Resource
    Online Resource
    The Kaggle book : data analysis and machine learning for competitive data science (2022)
    Birmingham : Packt Publishing
    Details
    ISBN: 1801812217 , 9781801812214 , 9781801817479
    Language: English
    Pages: 1 online resource (530 pages) , color illustrations.
    Edition: [First edition].
    Series Statement: Expert insight
    Parallel Title: Erscheint auch als
    DDC: 006.3/1
    Keywords: Machine learning ; Big data ; Electronic books ; Apprentissage automatique ; Données volumineuses ; Big data ; Machine learning ; Electronic books
    Abstract: Get a step ahead of your competitors with insights from over 30 Kaggle Masters and Grandmasters. Discover tips, tricks, and best practices for competing effectively on Kaggle and becoming a better data scientist. Key Features Learn how Kaggle works and how to make the most of competitions from over 30 expert Kagglers Sharpen your modeling skills with ensembling, feature engineering, adversarial validation and AutoML A concise collection of smart data handling techniques for modeling and parameter tuning Book Description Millions of data enthusiasts from around the world compete on Kaggle, the most famous data science competition platform of them all. Participating in Kaggle competitions is a surefire way to improve your data analysis skills, network with an amazing community of data scientists, and gain valuable experience to help grow your career. The first book of its kind, The Kaggle Book assembles in one place the techniques and skills you'll need for success in competitions, data science projects, and beyond. Two Kaggle Grandmasters walk you through modeling strategies you won't easily find elsewhere, and the knowledge they've accumulated along the way. As well as Kaggle-specific tips, you'll learn more general techniques for approaching tasks based on image, tabular, textual data, and reinforcement learning. You'll design better validation schemes and work more comfortably with different evaluation metrics. Whether you want to climb the ranks of Kaggle, build some more data science skills, or improve the accuracy of your existing models, this book is for you. What you will learn Get acquainted with Kaggle as a competition platform Make the most of Kaggle Notebooks, Datasets, and Discussion forums Create a portfolio of projects and ideas to get further in your career Design k-fold and probabilistic validation schemes Get to grips with common and never-before-seen evaluation metrics Understand binary and multi-class classification and object detection Approach NLP and time series tasks more effectively Handle simulation and optimization competitions on Kaggle Who this book is for This book is suitable for anyone new to Kaggle, veteran users, and anyone in between. Data analysts/scientists who are trying to do better in Kaggle competitions and secure jobs with tech giants will find this book useful. A basic understanding of machine learning concepts will help you make the most of this book.
    Note: Includes index
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  • 95
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    SECURITY AND PRIVACY TRENDS IN CLOUD COMPUTING AND BIG DATA (2022)
    [Erscheinungsort nicht ermittelbar] : CRC PRESS
    Details
    ISBN: 9781000583700 , 1000583708 , 9781003107286 , 1003107281 , 9781000583632 , 1000583635
    Language: English
    Pages: 1 online resource
    Parallel Title: Erscheint auch als
    Keywords: Cloud computing Security measures ; Big data ; Computer security ; Infonuagique ; Sécurité ; Mesures ; Données volumineuses ; Sécurité informatique ; COMPUTERS / Networking / Security ; COMPUTERS / Data Processing / General ; Big data ; Computer security
    Abstract: It is essential for an organization to know before involving themselves in cloud computing and big data, what are the key security requirements for applications and data processing. Big data and cloud computing are integrated together in practice. Cloud computing offers massive storage, high computation power, and distributed capability to support processing of big data. In such an integrated environment the security and privacy concerns involved in both technologies become combined. This book discusses these security and privacy issues in detail and provides necessary insights into cloud computing and big data integration. It will be useful in enhancing the body of knowledge concerning innovative technologies offered by the research community in the area of cloud computing and big data. Readers can get a better understanding of the basics of cloud computing, big data, and security mitigation techniques to deal with current challenges as well as future research opportunities.
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  • 96
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    Econometrics with Machine Learning (2022)
    Cham : Springer International Publishing | Cham : Imprint: Springer
    Details
    ISBN: 9783031151491
    Language: English
    Pages: 1 Online-Ressource (XXII, 371 p. 49 illus., 36 illus. in color.)
    Series Statement: Advanced Studies in Theoretical and Applied Econometrics 53
    Series Statement: Springer eBook Collection
    Parallel Title: Erscheint auch als
    Parallel Title: Erscheint auch als
    Parallel Title: Erscheint auch als
    Keywords: Econometrics. ; Machine learning. ; Macroeconomics. ; Machine Learning and causality ; Linear models ; Non-linear models ; Econometric forecasting and prediction ; Policy evaluation ; Network data ; Poverty ; Inequality ; Machine learning in Finance ; Empirical applications ; Testing statistical hypotheses ; Big data ; Econometric techniques ; Modelling macroeconomic relations ; Discrete Choice models ; Aufsatzsammlung ; Ökonometrie ; Maschinelles Lernen
    Abstract: Linear Econometric Models with Machine Learning -- Nonlinear Econometric Models with Machine Learning -- The Use of Machine Learning in Treatment Effect Estimation.-Forecasting with Machine Learning Methods.-Causal Estimation of Treatment Effects From Observational Health Care Data Using Machine Learning Methods -- Econometrics of Networks with Machine Learning -- Fairness in Machine Learning and Econometrics -- Graphical Models and their Interactions with Machine Learning in the Context of Economics and Finance -- Poverty, Inequality and Development Studies with Machine Learning -- Machine Learning for Asset Pricing.
    Abstract: This book helps and promotes the use of machine learning tools and techniques in econometrics and explains how machine learning can enhance and expand the econometrics toolbox in theory and in practice. Throughout the volume, the authors raise and answer six questions: 1) What are the similarities between existing econometric and machine learning techniques? 2) To what extent can machine learning techniques assist econometric investigation? Specifically, how robust or stable is the prediction from machine learning algorithms given the ever-changing nature of human behavior? 3) Can machine learning techniques assist in testing statistical hypotheses and identifying causal relationships in ‘big data? 4) How can existing econometric techniques be extended by incorporating machine learning concepts? 5) How can new econometric tools and approaches be elaborated on based on machine learning techniques? 6) Is it possible to develop machine learning techniques further and make them even more readily applicable in econometrics? As the data structures in economic and financial data become more complex and models become more sophisticated, the book takes a multidisciplinary approach in developing both disciplines of machine learning and econometrics in conjunction, rather than in isolation. This volume is a must-read for scholars, researchers, students, policy-makers, and practitioners, who are using econometrics in theory or in practice. .
    DOI: 10.1007/978-3-031-15149-1
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  • 97
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    Financial Data Analytics : Theory and Application (2022)
    Cham : Springer International Publishing | Cham : Imprint: Springer
    Details
    ISBN: 9783030837990
    Language: English
    Pages: 1 Online-Ressource (XXII, 384 p. 122 illus., 100 illus. in color.)
    Series Statement: Contributions to Finance and Accounting
    Series Statement: Springer eBook Collection
    Parallel Title: Erscheint auch als Financial data analytics
    Parallel Title: Erscheint auch als
    Parallel Title: Erscheint auch als
    Keywords: Finance. ; Business enterprises—Finance. ; Macroeconomics. ; Econometrics. ; Digitalization in finance ; Machine learning ; Predictive techniques ; Deep learning ; Natural Language Processing (NLP) ; Insurance business with data science ; Big data ; Cloud services ; Foreign currency exchange ; Prescriptive modeling techniques ; Blockchain ; Innovative technology ; Optimization of regulatory portfolios ; Financial networks ; Emerging markets ; Financial sector ; Time series analysis ; Financial econometrics ; Aufsatzsammlung ; Kreditmarkt ; Datenanalyse ; Ökonometrie
    Abstract: PART 1. INTRODUCTION AND ANALYTICS MODELS -- Retraining and Reskilling Financial Participators in the Digital Age -- Basics of Financial Data Analytics -- Predictive Analytics Techniques: Theory and Applications in Finance -- Prescriptive Analytics Techniques: Theory and Applications in Finance -- Forecasting Returns of Crypto Currency - Analyzing Robustness of Auto Regressive and Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANNS) -- PART 2. MACHINE LEARNING -- Machine Learning in Financial Markets: Dimension Reduction and Support Vector Machine -- Pruned Random Forests for Effective and Efficient Financial Data Analytics -- Foreign Currency Exchange Rate Prediction Using Long Short Term Memory -- Natural Language Processing (NLP) for Exploring Culture in Finance: Theory and Applications -- PART 3. TECHNOLOGY DRIVEN FINANCE -- Financial Networks: A Review of Models and the Use of Network Similarities -- Optimization of Regulatory Economic-Capital Structured Portfolios: Modeling Algorithms, Financial Data Analytics and Reinforcement Machine Learning in Emerging Markets -- Transforming Insurance Business with Data Science -- A General Cyber Hygiene Approach for Financial Analytical Environment.
    Abstract: This book presents both theory of financial data analytics, as well as comprehensive insights into the application of financial data analytics techniques in real financial world situations. It offers solutions on how to logically analyze the enormous amount of structured and unstructured data generated every moment in the finance sector. This data can be used by companies, organizations, and investors to create strategies, as the finance sector rapidly moves towards data-driven optimization. This book provides an efficient resource, addressing all applications of data analytics in the finance sector. International experts from around the globe cover the most important subjects in finance, including data processing, knowledge management, machine learning models, data modeling, visualization, optimization for financial problems, financial econometrics, financial time series analysis, project management, and decision making. The authors provide empirical evidence as examples of specific topics. By combining both applications and theory, the book offers a holistic approach. Therefore, it is a must-read for researchers and scholars of financial economics and finance, as well as practitioners interested in a better understanding of financial data analytics. .
    DOI: 10.1007/978-3-030-83799-0
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  • 98
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    Big Data in Finance : Opportunities and Challenges of Financial Digitalization (2022)
    Cham : Springer International Publishing | Cham : Imprint: Palgrave Macmillan
    Details
    ISBN: 9783031122408
    Language: English
    Pages: 1 Online-Ressource (XXV, 272 p. 41 illus., 31 illus. in color.)
    Series Statement: Springer eBook Collection
    Parallel Title: Erscheint auch als
    Parallel Title: Erscheint auch als
    Parallel Title: Erscheint auch als
    Parallel Title: Erscheint auch als Big data in finance
    RVK:
    QK 600
    RVK:
    QH 500
    RVK:
    ST 530
    Keywords: Big Data ; Data Mining ; Finanzdienstleistung ; Finanzsektor ; Finanztechnologie ; Künstliche Intelligenz ; Digitalisierung ; Financial engineering. ; Big data. ; Big data ; Artificial intelligence ; Quantitative trading ; Financial services ; Deep learning ; FinTech ; Aufsatzsammlung
    Abstract: Chapter 1: Big Data in Finance: An Overview -- SECTION I: BIG DATA IN THE FINANCIAL MARKETS -- Chapter 2: Alternative Data -- Chapter 3: An Algorithmic Trading Strategy to Balance Profitability and Risk -- Chapter 4: High-Frequency Trading and Market Efficiency in the Moroccan Stock Market -- Chapter 5: Ensemble Models using Symbolic Regression and Genetic Programming for Uncertainty Estimation in ESG and Alternative Investments -- SECTION II: BIG DATA IN FINANCIAL SERVICES -- Chapter 6: Consumer Credit Assessments in the Age of Big Data -- Chapter 7; Robo-Advisors: A Big Data Challenge -- Chapter 8: Bitcoin: Future or Fad? -- Chapter 9: Culture, Digital Assets, and the Economy: A Trans-National Perspective -- SECTION III: CASE STUDIES AND APPLICATIONS -- Chapter 10: Islamic Finance in Canada Powered by Big Data: A Case Study -- Chapter 11: Assessing the Carbon Footprint of Cryptoassets: Evidence from a Bivariate VAR Model -- Chapter 12:A Data-informed Approach to Financial Literacy Enhancement using Cognitive & Behavioral Analytics.
    Abstract: This edited book explores the unique risks, opportunities, challenges, and societal implications associated with big data developments within the field of finance. While the general use of big data has been the subject of frequent discussions, this book will take a more focused look at big data applications in the financial sector. With contributions from researchers, practitioners, and entrepreneurs involved at the forefront of big data in finance, the book discusses technological and business-inspired breakthroughs in the field. The contributions offer technical insights into the different applications presented and highlight how these new developments may impact and contribute to the evolution of the financial sector. Additionally, the book presents several case studies that examine practical applications of big data in finance. In exploring the readiness of financial institutions to adapt to new developments in the big data/artificial intelligence space and assessing different implementation strategies and policy solutions, the book will be of interest to academics, practitioners, and regulators who work in this field. Thomas Walker is a Full Professor of Finance and the Concordia University Research Chair in Emerging Risk Management at Concordia University, Montreal, Canada. Prior to academia, he worked for several years in the German consulting and industrial sector at Mercedes Benz, Utility Consultants International, Lahmeyer International, Telenet, and KPMG Peat Marwick. Frederick Davis is an Associate Professor at the John Molson School of Business at Concordia University, Montreal, Canada. Prior to his academic career, he worked for several years in the government sector assisting communities with their economic development. His research interests include mergers and acquisitions, insider trading, big data, and other aspects of corporate finance. Tyler Schwartz holds an MSc degree in Data Science and Business Analytics from HEC Montreal. He has served as a research assistant in the Department of Finance at Concordia University for over four years and is the co-author of an edited book collection on climate change adaptation as well as working papers on social impact bonds and the Sustainable Development Goals (SDGs).
    DOI: 10.1007/978-3-031-12240-8
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  • 99
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    Artificial Intelligence and Environmental Sustainability : Challenges and Solutions in the Era of Industry 4.0 (2022)
    Singapore : Springer Nature Singapore | Cham : Springer International Publishing AG
    Details
    ISBN: 9789811914348 , 9811914346
    Language: English
    Pages: 1 Online-Ressource (XIII, 211 Seiten) , 59 illus., 53 illus. in color.
    Edition: 1st ed. 2022
    Series Statement: Algorithms for Intelligent Systems
    Parallel Title: Erscheint auch als Artificial Intelligence and Environmental Sustainability
    DDC: 006.3
    Keywords: Computational intelligence ; Artificial intelligence ; Big data ; Computational Intelligence ; Artificial Intelligence ; Big Data
    DOI: 10.1007/978-981-19-1434-8
    URL: https://doi.org/10.1007/978-981-19-1434-8
    URL: Inhaltsverzeichnis
    URL: Ausführliche Beschreibung
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  • 100
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    Research Methodologies and Ethical Challenges in Digital Migration Studies : Caring For (Big) Data? (2022)
    Cham : Springer International Publishing | Imprint: Palgrave Macmillan
    Details
    ISBN: 9783030812263
    Language: English
    Pages: 1 Online-Ressource (XVI, 262 p. 7 illus. in color)
    Edition: 1st ed. 2022
    Series Statement: Approaches to Social Inequality and Difference
    Parallel Title: Printed edition
    Parallel Title: Printed edition
    Parallel Title: Printed edition
    DDC: 304.8
    Keywords: Emigration and immigration ; Social media ; Digital media ; Humanities—Digital libraries ; Big data
    DOI: 10.1007/978-3-030-81226-3
    URL: https://doi.org/10.1007/978-3-030-81226-3
    URL: kostenfrei
    URL: Inhaltsverzeichnis
    URL: Ausführliche Beschreibung
    URL: kostenfrei
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