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  • Sebastopol, CA : O'Reilly Media  (77)
  • Big data  (45)
  • Java (Computer program language)  (32)
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  • 1
    Online-Ressource
    Online-Ressource
    Data lake maturity model : charting a course for data success (2019)
    Sebastopol, CA : O'Reilly Media
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 volume) , illustrations
    Ausgabe: First edition.
    Schlagwort(e): Electronic data processing ; Distributed processing ; Management ; Big data ; Information storage and retrieval systems ; Electronic books ; Electronic books ; local
    Kurzfassung: Data is changing everything. Many industries today are being fundamentally transformed through the accumulation and analysis of large quantities of data, stored in diversified but flexible repositories known as data lakes. Whether your company has just begun to think about big data or has already initiated a strategy for handling it, this practical ebook shows you how to plan a successful data lake migration. You'll learn the value of data lakes, their structure, and the problems they attempt to solve. Using Zaloni's data lake maturity model, you'll then explore your organization's readiness for putting a data lake into action. Do you have the tools and data architectures to support big data analysis? Are your people and processes prepared? The data lake maturity model will help you rate your organization's readiness. This report includes: The structure and purpose of a data lake Descriptive, predictive, and prescriptive analytics Data lake curation, self-service, and the use of data lake zones How to rate your organization using the data lake maturity model A complete checklist to help you determine your strategic path forward
    Anmerkung: Description based on online resource; title from title page (viewed June 4, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781491996560/?ar
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  • 2
    Online-Ressource
    Online-Ressource
    Stream processing with Apache Flink : fundamentals, implementation, and operation of streaming applications (2019)
    Sebastopol, CA : O'Reilly Media
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 volume) , illustrations
    Ausgabe: First edition.
    Schlagwort(e): Apache Flink ; Streaming technology (Telecommunications) ; Big data ; Electronic books ; Electronic books ; local
    Kurzfassung: Get started with Apache Flink, the open source framework that powers some of the world's largest stream processing applications. With this practical book, you'll explore the fundamental concepts of parallel stream processing and discover how this technology differs from traditional batch data processing. Longtime Apache Flink committers Fabian Hueske and Vasia Kalavri show you how to implement scalable streaming applications with Flink's DataStream API and continuously run and maintain these applications in operational environments. Stream processing is ideal for many use cases, including low-latency ETL, streaming analytics, and real-time dashboards as well as fraud detection, anomaly detection, and alerting. You can process continuous data of any kind, including user interactions, financial transactions, and IoT data, as soon as you generate them. Learn concepts and challenges of distributed stateful stream processing Explore Flink's system architecture, including its event-time processing mode and fault-tolerance model Understand the fundamentals and building blocks of the DataStream API, including its time-based and statefuloperators Read data from and write data to external systems with exactly-once consistency Deploy and configure Flink clusters Operate continuously running streaming applications
    Anmerkung: Includes bibliographical references and index. - Description based on online resource; title from title page (Safari, viewed April 24, 2019)
    URL: lizenzpflichtig
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  • 3
    Online-Ressource
    Online-Ressource
    Stream processing with Apache Spark : mastering structured streaming and Spark streaming (2019)
    Sebastopol, CA : O'Reilly Media
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 volume) , illustrations
    Ausgabe: First edition.
    Schlagwort(e): Spark (Electronic resource : Apache Software Foundation) ; Electronic data processing ; Distributed processing ; Data mining ; Big data ; Electronic books ; Electronic books ; local
    Kurzfassung: Before you can build analytics tools to gain quick insights, you first need to know how to process data in real time. With this practical guide, developers familiar with Apache Spark will learn how to put this in-memory framework to use for streaming data. You'll discover how Spark enables you to write streaming jobs in almost the same way you write batch jobs. Authors Gerard Maas and François Garillot help you explore the theoretical underpinnings of Apache Spark. This comprehensive guide features two sections that compare and contrast the streaming APIs Spark now supports: the original Spark Streaming library and the newer Structured Streaming API. Learn fundamental stream processing concepts and examine different streaming architectures Explore Structured Streaming through practical examples; learn different aspects of stream processing in detail Create and operate streaming jobs and applications with Spark Streaming ; integrate Spark Streaming with other Spark APIs Learn advanced Spark Streaming techniques, including approximation algorithms and machine learning algorithms Compare Apache Spark to other stream processing projects, including Apache Storm, Apache Flink, and Apache Kafka Streams
    Anmerkung: Includes bibliographical references and index. - Description based on online resource; title from title page (Safari, viewed June 11, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781491944233/?ar
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  • 4
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    Online-Ressource
    Java in a nutshell (2018)
    Sebastopol, CA : O'Reilly Media
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 volume) , illustrations
    Ausgabe: Seventh edition.
    Schlagwort(e): Java (Computer program language) ; Web servers ; Object-oriented programming (Computer science) ; Electronic books ; Electronic books ; local
    Kurzfassung: This updated edition of Java in a Nutshell not only helps experienced Java programmers get the most out of Java versions 9 through 11, it's also a learning path for new developers. Chock full of examples that demonstrate how to take complete advantage of modern Java APIs and development best practices, this thoroughly revised book includes new material on Java Concurrency Utilities. The book's first section provides a fast-paced, no-fluff introduction to the Java programming language and the core runtime aspects of the Java platform. The second section is a reference to core concepts and APIs that explains how to perform real programming work in the Java environment. Get up to speed on language details, including Java 9-11 changes Learn object-oriented programming, using basic Java syntax Explore generics, enumerations, annotations, and lambda expressions Understand basic techniques used in object-oriented design Examine concurrency and memory, and how they're intertwined Work with Java collections and handle common data formats Delve into Java's latest I/O APIs, including asynchronous channels Use Nashorn to execute JavaScript on the Java Virtual Machine Become familiar with development tools in OpenJDK
    Anmerkung: Includes index. - Description based on online resource; title from title page (viewed February 4, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781492037248/?ar
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  • 5
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    Online-Ressource
    Cleaning up the data lake with an operational data hub (2018)
    Sebastopol, CA : O'Reilly Media
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 volume) , illustrations
    Ausgabe: First edition.
    Schlagwort(e): Database management ; Information storage and retrieval systems ; Electronic data processing ; Big data ; Electronic books ; Electronic books ; local
    Kurzfassung: The data lake was once heralded as the answer to the flood of big data that arrived in a variety of structured and unstructured formats. But, due to the ease of integration and the lack of governance, data lakes in many companies have devolved into unusable data swamps. This short ebook shows you how to solve this problem using an Operational Data Hub (ODH) to collect, store, index, cleanse, harmonize, and master data of all shapes and formats. Gerhard Ungerer-CTO and co-founder of Random Bit LLC-explains how the ODH supports transactional integrity so that the hub can serve as integration point for enterprise applications. You'll also learn how the ODH helps you leverage the investment in your data lake (or swamp), so that the data trapped there can finally be ingested, processed, and provisioned. With this ebook, you'll learn how an ODH: Allows you to focus on categorizing data for easy and fast retrieval Provides flexible storage models, indexing support, query capabilities, security, and a governance framework Delivers flexible storage models; support for indexing, scripting, and automation; query capabilities; transactional integrity; and security Includes a governance model to help you access, ingest, harmonize, materialize, provision, and consume data
    Anmerkung: Description based on online resource; title from title page (viewed January 10, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781492027386/?ar
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  • 6
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    Online-Ressource
    Getting started with Kudu : perform fast analytics on fast data (2018)
    Sebastopol, CA : O'Reilly Media
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 volume) , illustrations
    Ausgabe: First edition.
    Schlagwort(e): Apache Hadoop ; Electronic data processing ; Distributed processing ; Big data ; Real-time data processing ; Database management ; Electronic books ; Electronic books ; local
    Kurzfassung: Fast data ingestion, serving, and analytics in the Hadoop ecosystem have forced developers and architects to choose solutions using the least common denominator-either fast analytics at the cost of slow data ingestion or fast data ingestion at the cost of slow analytics. There is an answer to this problem. With the Apache Kudu column-oriented data store, you can easily perform fast analytics on fast data. This practical guide shows you how. Begun as an internal project at Cloudera, Kudu is an open source solution compatible with many data processing frameworks in the Hadoop environment. In this book, current and former solutions professionals from Cloudera provide use cases, examples, best practices, and sample code to help you get up to speed with Kudu. Explore Kudu's high-level design, including how it spreads data across servers Fully administer a Kudu cluster, enable security, and add or remove nodes Learn Kudu's client-side APIs, including how to integrate Apache Impala, Spark, and other frameworks for data manipulation Examine Kudu's schema design, including basic concepts and primitives necessary to make your project successful Explore case studies for using Kudu for real-time IoT analytics, predictive modeling, and in combination with another storage engine
    Anmerkung: Includes index. - Description based on online resource; title from title page (Safari, viewed April 10, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781491980248/?ar
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  • 7
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    Online-Ressource
    Streaming change data capture : a foundation for modern data architectures (2018)
    Sebastopol, CA : O'Reilly Media
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 volume) , illustrations
    Ausgabe: First edition.
    Schlagwort(e): Computer architecture ; Software architecture ; Electronic data processing ; Software engineering ; Big data ; Electronic books ; Electronic books ; local
    Kurzfassung: There are many benefits to becoming a data-driven organization, including the ability to accelerate and improve business decision accuracy through the real-time processing of transactions, social media streams, and IoT data. But those benefits require significant changes to your infrastructure. You need flexible architectures that can copy data to analytics platforms at near-zero latency while maintaining 100% production uptime. Fortunately, a solution already exists. This ebook demonstrates how change data capture (CDC) can meet the scalability, efficiency, real-time, and zero-impact requirements of modern data architectures. Kevin Petrie, Itamar Ankorion, and Dan Potter-technology marketing leaders at Attunity-explain how CDC enables faster and more accurate decisions based on current data and reduces or eliminates full reloads that disrupt production and efficiency. The book examines: How CDC evolved from a niche feature of database replication software to a critical data architecture building block Architectures where data workflow and analysis take place, and their integration points with CDC How CDC identifies and captures source data updates to assist high-speed replication to one or more targets Case studies on cloud-based streaming and streaming to a data lake and related architectures Guiding principles for effectively implementing CDC in cloud, data lake, and streaming environments The Attunity Replicate platform for efficiently loading data across all major database, data warehouse, cloud, streaming, and Hadoop platforms
    Anmerkung: Description based on online resource; title from title page (Safari, viewed July 2, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781492032526/?ar
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  • 8
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    Online-Ressource
    Foundations for architecting data solutions : managing successful data projects (2018)
    Sebastopol, CA : O'Reilly Media
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 volume) , illustrations
    Ausgabe: First edition.
    Schlagwort(e): Big data ; Database management ; Electronic books ; Electronic books ; local
    Kurzfassung: While many companies ponder implementation details such as distributed processing engines and algorithms for data analysis, this practical book takes a much wider view of big data development, starting with initial planning and moving diligently toward execution. Authors Ted Malaska and Jonathan Seidman guide you through the major components necessary to start, architect, and develop successful big data projects. Everyone from CIOs and COOs to lead architects and developers will explore a variety of big data architectures and applications, from massive data pipelines to web-scale applications. Each chapter addresses a piece of the software development life cycle and identifies patterns to maximize long-term success throughout the life of your project. Start the planning process by considering the key data project types Use guidelines to evaluate and select data management solutions Reduce risk related to technology, your team, and vague requirements Explore system interface design using APIs, REST, and pub/sub systems Choose the right distributed storage system for your big data system Plan and implement metadata collections for your data architecture Use data pipelines to ensure data integrity from source to final storage Evaluate the attributes of various engines for processing the data you collect
    Anmerkung: Includes index. - Description based on online resource; title from title page (Safari, viewed March 11, 2019)
    URL: lizenzpflichtig
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  • 9
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    Architecting data lakes : data management architectures for advanced business use cases (2018)
    Sebastopol, CA : O'Reilly Media
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 volume) , illustrations
    Ausgabe: Second edition.
    Schlagwort(e): Big data ; Database management ; Business enterprises ; Data processing ; Information technology ; Management ; Electronic books ; Electronic books ; local
    Kurzfassung: Many organizations today are succeeding with data lakes, not just as storage repositories but as places to organize, prepare, analyze, and secure a wide variety of data. Management and governance is critical for making your data lake work, yet hard to do without a roadmap. With this ebook, you'll learn an approach that merges the flexibility of a data lake with the management and governance of a traditional data warehouse. Author Ben Sharma explains the steps necessary to deploy data lakes with robust, metadata-driven data management platforms. You'll learn best practices for building, maintaining, and deriving value from a data lake in your production environment. Included is a detailed checklist to help you construct a data lake in a controlled yet flexible way. Managing and governing data in your lake cannot be an afterthought. This ebook explores how integrated data lake management solutions, such as the Zaloni Data Platform (ZDP), deliver necessary controls without making data lakes slow and inflexible. You'll examine: A reference architecture for a production-ready data lake An overview of the data lake technology stack and deployment options Key data lake attributes, including ingestion, storage, processing, and access Why implementing management and governance is crucial for the success of your data lake How to curate data lakes through data governance, acquisition, organization, preparation, and provisioning Methods for providing secure self-service access for users across the enterprise How to build a future-proof data lake tech stack that includes storage, processing, data management, and reference architecture Emerging trends that will shape the future of data lakes
    Anmerkung: Previous edition published: 2016. - Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed January 8, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781492033004/?ar
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  • 10
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    Fast data use cases for telecommunications : how fast data can help telcos virtualize, monetize, and deal with the data deluge (2018)
    Sebastopol, CA : O'Reilly Media
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 volume) , illustrations
    Ausgabe: First edition.
    Schlagwort(e): Big data ; Streaming technology (Telecommunications) ; Internet of things ; Application software ; Development ; Storage area networks (Computer networks) ; Business enterprises ; Computer networks ; Electronic books ; Electronic books ; local
    Kurzfassung: Telecommunications networks have always generated data at line speed, but with the massive increase in streaming data these service providers now handle, telcos have to process and act on data in milliseconds. Fast data-not just big data-is the future of telco. This report examines several use cases that illustrate telco's increasing need for fast data in operational and business support (OSS and BSS) systems, flexible NFV and 5G architectures, subscription services, and IoT devices. You'll explore case studies involving VoltDB, an in-memory, NewSQL database popular with telcos for its ability to handle the speed and scale of fast data. This report reflects the experiences of VoltDB engineers and their telco customers-Openet, Nokia, Emagine International, and Nimble Storage-that recently deployed flexible, cost-effective fast-data solutions. Discover how: Fast-data systems ingest, analyze, act upon, and export data while meeting stringent non-functional requirements Openet evolved its mediation product with a VoltDB system to process 1 trillion real-time events per day Nokia seamlessly transitioned to NFV and SDN to deliver converged broadband and IoT communication Emagine used VoltDB to provide real-time analysis of network subscriber data based on event triggers Nimble upgraded its predictive analytics platform to predict, diagnose, and prevent performance problems in IoT storage arrays
    Anmerkung: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed December 6, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781491998267/?ar
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  • 11
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    Optimizing Java : practical techniques for improving JVM application performance (2018)
    Sebastopol, CA : O'Reilly Media
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 volume) , illustrations
    Ausgabe: First edition.
    Schlagwort(e): Java (Computer program language) ; Virtual computer systems ; Electronic books ; Electronic books ; local
    Kurzfassung: Performance tuning is an experimental science, but that doesn't mean engineers should resort to guesswork and folklore to get the job done. Yet that's often the case. With this practical book, intermediate to advanced Java technologists working with complex technology stacks will learn how to tune Java applications for performance using a quantitative, verifiable approach. Most resources on performance tend to discuss the theory and internals of Java virtual machines, but this book focuses on the practicalities of performance tuning by examining a wide range of aspects. There are no simple recipes, tips and tricks, or algorithms to learn. Performance tuning is a process of defining and determining desired outcomes. And it requires diligence. Learn how Java principles and technology make the best use of modern hardware and operating systems Explore several performance tests and common anti-patterns that can vex your team Understand the pitfalls of measuring Java performance numbers and the drawbacks of microbenchmarking Dive into JVM garbage collection logging, monitoring, tuning, and tools Explore JIT compilation and Java language performance techniques Learn performance aspects of the Java Collections API and get an overview of Java concurrency
    Anmerkung: Includes index. - Description based on online resource; title from title page (Safari, viewed June 25, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781492039259/?ar
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  • 12
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    Creating a data-driven enterprise in media : DataOps insights from Comcast, SlingTV, and Turner Broadcasting (2018)
    Sebastopol, CA : O'Reilly Media
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 volume) , illustrations
    Ausgabe: First edition.
    Schlagwort(e): Cultural industries ; Data processing ; Information technology ; Management ; Decision making ; Data processing ; Big data ; Electronic books ; Electronic books ; local
    Kurzfassung: The data-driven revolution is finally hitting the media and entertainment industry. For decades, broadcast television and print media relied on traditional delivery channels for solvency and growth, but those channels fragmented as cable, streaming, and digital devices stole the show. In this ebook, you'll learn about the trends, challenges, and opportunities facing players in this industry as they tackle big data, advanced analytics, and DataOps. You'll explore best practices and lessons learned from three real-world media companies-Sling TV, Turner Broadcasting, and Comcast-as they proceed on their data-driven journeys. Along the way, authors Ashish Thusoo and Joydeep Sen Sarma explain how DataOps breaks down silos and connects everyone who handles data, including engineers, data scientists, analysts, and business users. Big-data-as-a-service provider Qubole provides a five-step maturity model that outlines the phases that a company typically goes through when it first encounters big data. Case studies include: Sling TV: this live streaming content platform delivers live TV and on-demand entertainment instantly to a variety of smart televisions, tablets, game consoles, computers, smartphones, and streaming devices Turner Broadcasting System: this Time Warner division recently created the Turner Data Cloud to support direct-to-consumer services, including FilmStruck, Boom (for kids), and NBA League Pass Comcast: the largest broadcasting and cable TV company is building a single integrated big data platform to deliver internet, TV, and voice to more than 28 million customers
    Anmerkung: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed January 9, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781491998007/?ar
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  • 13
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    Continuous delivery in Java : essential tools and best practices for deploying code to production (2018)
    Sebastopol, CA : O'Reilly Media
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 volume) , illustrations
    Ausgabe: First edition.
    Schlagwort(e): Application software ; Development ; Automation ; Java (Computer program language) ; Electronic books ; Electronic books ; local
    Kurzfassung: Continuous delivery adds enormous value to the business and the entire software delivery lifecycle, but adopting this practice means mastering new skills typically outside of a developer's comfort zone. In this practical book, Daniel Bryant and Abraham Marín-Pérez provide guidance to help experienced Java developers master skills such as architectural design, automated quality assurance, and application packaging and deployment on a variety of platforms. Not only will you learn how to create a comprehensive build pipeline for continually delivering effective software, but you'll also explore how Java application architecture and deployment platforms have affected the way we rapidly and safely deliver new software to production environments. Get advice for beginning or completing your migration to continuous delivery Design architecture to enable the continuous delivery of Java applications Build application artifacts including fat JARs, virtual machine images, and operating system container (Docker) images Use continuous integration tooling like Jenkins, PMD, and find-sec-bugs to automate code quality checks Create a comprehensive build pipeline and design software to separate the deploy and release processes Explore why functional and system quality attribute testing is vital from development to delivery Learn how to effectively build and test applications locally and observe your system while it runs in production
    Anmerkung: Includes bibliographical references and index. - Description based on online resource; title from title page (Safari, viewed November 28, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781491986011/?ar
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  • 14
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    Fast data architectures for streaming applications : getting answers now from data sets that never end (2018)
    Sebastopol, CA : O'Reilly Media
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 volume) , illustrations
    Ausgabe: Second edition.
    Schlagwort(e): Service-oriented architecture (Computer science) ; Application software ; Development ; Data mining ; Big data ; Electronic books ; Electronic books ; local
    Kurzfassung: Why have stream-oriented data systems become so popular, when batch-oriented systems have served big data needs for many years? In the updated edition of this report, Dean Wampler examines the rise of streaming systems for handling time-sensitive problems-such as detecting fraudulent financial activity as it happens. You'll explore the characteristics of fast data architectures, along with several open source tools for implementing them. Batch processing isn't going away, but exclusive use of these systems is now a competitive disadvantage. You'll learn that, while fast data architectures using tools such as Kafka, Akka, Spark, and Flink are much harder to build, they represent the state of the art for dealing with mountains of data that require immediate attention. Learn how a basic fast data architecture works, step-by-step Examine how Kafka's data backplane combines the best abstractions of log-oriented and message queue systems for integrating components Evaluate four streaming engines, including Kafka Streams, Akka Streams, Spark, and Flink Learn which streaming engines work best for different use cases Get recommendations for making real-world streaming systems responsive, resilient, elastic, and message driven Explore an example IoT streaming application that includes telemetry ingestion and anomaly detection
    Anmerkung: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed January 30, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781492046820/?ar
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  • 15
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    Ethics and data science (2018)
    Sebastopol, CA : O'Reilly Media
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 volume)
    Ausgabe: First edition.
    Schlagwort(e): Data mining ; Moral and ethical aspects ; Big data ; Machine learning ; Quantitative research ; Electronic books ; local
    Kurzfassung: As the impact of data science continues to grow on society there is an increased need to discuss how data is appropriately used and how to address misuse. Yet, ethical principles for working with data have been available for decades. The real issue today is how to put those principles into action. With this report, authors Mike Loukides, Hilary Mason, and DJ Patil examine practical ways for making ethical data standards part of your work every day. To help you consider all of possible ramifications of your work on data projects, this report includes: A sample checklist that you can adapt for your own procedures Five framing guidelines (the Five C's) for building data products: consent, clarity, consistency, control, and consequences Suggestions for building ethics into your data-driven culture Now is the time to invest in a deliberate practice of data ethics, for better products, better teams, and better outcomes. Get a copy of this report and learn what it takes to do good data science today.
    Anmerkung: Description based on online resource; title from title page (Safari, viewed August 17, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781492043898/?ar
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  • 16
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    Cloud native Java : designing resilient systems with Spring Boot, Spring Cloud, and Cloud Foundry (2017)
    Sebastopol, CA : O'Reilly Media
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 volume) , illustrations
    Ausgabe: First edition.
    Schlagwort(e): Java (Computer program language) ; Cloud computing ; Electronic books ; Electronic books ; local
    Kurzfassung: What separates the traditional enterprise from the likes of Amazon, Netflix, and Etsy? Those companies have refined the art of cloud native development to maintain their competitive edge and stay well ahead of the competition. This practical guide shows Java/JVM developers how to build better software, faster, using Spring Boot, Spring Cloud, and Cloud Foundry. Many organizations have already waded into cloud computing, test-driven development, microservices, and continuous integration and delivery. Authors Josh Long and Kenny Bastani fully immerse you in the tools and methodologies that will help you transform your legacy application into one that is genuinely cloud native. In four sections, this book takes you through: The Basics: learn the motivations behind cloud native thinking; configure and test a Spring Boot application; and move your legacy application to the cloud Web Services: build HTTP and RESTful services with Spring; route requests in your distributed system; and build edge services closer to the data Data Integration: manage your data with Spring Data, and integrate distributed services with Spring's support for event-driven, messaging-centric architectures Production: make your system observable; use service brokers to connect stateful services; and understand the big ideas behind continuous delivery
    Anmerkung: Includes index. - Description based on online resource; title from title page (Safari, viewed August 21, 2017)
    URL: https://learning.oreilly.com/library/view/-/9781449374631/?ar
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  • 17
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    Spock, up and running : writing expressive tests in Java and Groovy (2017)
    Sebastopol, CA : O'Reilly Media
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 volume)
    Ausgabe: First edition.
    Schlagwort(e): Computer software ; Testing ; Java (Computer program language) ; Groovy (Computer program language) ; Electronic books ; Electronic books ; local
    Kurzfassung: Most developers would agree that writing automated tests is a good idea, but writing good, well-structured tests is still an elusive skill for many. For Java and Groovy developers, however, there's good news. This practical guide shows you how to write concise and highly readable tests with Spock, the most innovative testing and specification framework for the JVM since JUnit. Author Rob Fletcher takes you from Spock basics to advanced topics, using fully worked integration examples. Through the course of this book, you'll build a simple web application-Squawker-that allows users to post short messages. You'll discover how much easier it is to write automated tests with Spock's straightforward and expressive language. Start by learning how to write simple unit tests Understand the lifecycle of Spock specifications and feature methods Dive into interaction testing, using Spock's intuitive syntax for dealing with mocks and stubs Learn about parameterized tests-writing feature methods that run for multiple sets of data Move into advanced topics, such as writing idiomatic Spock code and driving parameterized tests with file or database input Learn how everything works together in a standalone, fully-worked, test-driven development example
    Anmerkung: Includes bibliographical references and index. - Description based on online resource; title from title page (viewed May 12, 2017)
    URL: https://learning.oreilly.com/library/view/-/9781491923283/?ar
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  • 18
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    High performance Spark : best practices for scaling and optimizing Apache Spark (2017)
    Sebastopol, CA : O'Reilly Media
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 volume) , illustrations
    Ausgabe: First edition.
    Schlagwort(e): Spark (Electronic resource : Apache Software Foundation) ; Big data ; Data mining ; Computer programs ; Electronic books ; Electronic books ; local
    Kurzfassung: Apache Spark is amazing when everything clicks. But if you haven't seen the performance improvements you expected, or still don't feel confident enough to use Spark in production, this practical book is for you. Authors Holden Karau and Rachel Warren demonstrate performance optimizations to help your Spark queries run faster and handle larger data sizes, while using fewer resources. Ideal for software engineers, data engineers, developers, and system administrators working with large-scale data applications, this book describes techniques that can reduce data infrastructure costs and developer hours. Not only will you gain a more comprehensive understanding of Spark, you'll also learn how to make it sing. With this book, you'll explore: How Spark SQL's new interfaces improve performance over SQL's RDD data structure The choice between data joins in Core Spark and Spark SQL Techniques for getting the most out of standard RDD transformations How to work around performance issues in Spark's key/value pair paradigm Writing high-performance Spark code without Scala or the JVM How to test for functionality and performance when applying suggested improvements Using Spark MLlib and Spark ML machine learning libraries Spark's Streaming components and external community packages
    Anmerkung: Includes index. - Description based on online resource; title from title page (Safari, viewed June 12, 2017)
    URL: https://learning.oreilly.com/library/view/-/9781491943199/?ar
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  • 19
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    Modern Java recipes : simple solutions to difficult problems in Java 8 and 9 (2017)
    Sebastopol, CA : O'Reilly Media
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 volume) , illustrations
    Ausgabe: First edition.
    Schlagwort(e): Java (Computer program language) ; Electronic books ; Electronic books ; local
    Kurzfassung: The introduction of functional programming concepts in Java SE 8 was a drastic change for this venerable object-oriented language. Lambda expressions, method references, and streams fundamentally changed the idioms of the language, and many developers have been trying to catch up ever since. This cookbook will help. With more than 70 detailed recipes, author Ken Kousen shows you how to use the newest features of Java to solve a wide range of problems. For developers comfortable with previous Java versions, this guide covers nearly all of Java SE 8, and includes a chapter focused on changes coming in Java 9. Need to understand how functional idioms will change the way you write code? This cookbook-chock full of use cases-is for you. Recipes cover: The basics of lambda expressions and method references Interfaces in the java.util.function package Stream operations for transforming and filtering data Comparators and Collectors for sorting and converting streaming data Combining lambdas, method references, and streams Creating instances and extract values from Java's Optional type New I/O capabilities that support functional streams The Date-Time API that replaces the legacy Date and Calendar classes Mechanisms for experimenting with concurrency and parallelism
    Anmerkung: Includes bibliographical references and index. - Description based on online resource; title from title page (Safari, viewed August 21, 2017)
    URL: https://learning.oreilly.com/library/view/-/9781491973165/?ar
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  • 20
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    Advanced analytics with Spark : patterns from learning from data at scale (2017)
    Sebastopol, CA : O'Reilly Media
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 volume) , illustrations
    Ausgabe: Second edition.
    Schlagwort(e): Spark (Electronic resource : Apache Software Foundation) ; Big data ; Data mining ; Computer programs ; Electronic books ; Electronic books ; local
    Kurzfassung: In the second edition of this practical book, four Cloudera data scientists present a set of self-contained patterns for performing large-scale data analysis with Spark. The authors bring Spark, statistical methods, and real-world data sets together to teach you how to approach analytics problems by example. Updated for Spark 2.1, this edition acts as an introduction to these techniques and other best practices in Spark programming. You'll start with an introduction to Spark and its ecosystem, and then dive into patterns that apply common techniques-including classification, clustering, collaborative filtering, and anomaly detection-to fields such as genomics, security, and finance. If you have an entry-level understanding of machine learning and statistics, and you program in Java, Python, or Scala, you'll find the book's patterns useful for working on your own data applications. With this book, you will: Familiarize yourself with the Spark programming model Become comfortable within the Spark ecosystem Learn general approaches in data science Examine complete implementations that analyze large public data sets Discover which machine learning tools make sense for particular problems Acquire code that can be adapted to many uses
    Anmerkung: Previous edition published: 2015. - Includes index. - Description based on online resource; title from title page (Safari, viewed June 19, 2017)
    URL: lizenzpflichtig
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  • 21
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    Containerizing continuous delivery in Java : Docker integration for build pipelines and application architecture (2017)
    Sebastopol, CA : O'Reilly Media
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 volume) , illustrations
    Ausgabe: First edition.
    Schlagwort(e): Application software ; Development ; Automation ; Java (Computer program language) ; Electronic books ; Electronic books ; local
    Kurzfassung: Using containers to package and deploy applications is causing a seismic shift in the way software is developed, and you may wonder how, or if, Java works with this new paradigm. In fact, combining Java with container technology can bring out the best in both. With this report, you'll learn valuable techniques, methodologies, and advice for continuously delivering Java applications with containers, from both an architectural and operational perspective. To help you follow the book's examples, author Daniel Bryant includes a simple ecommerce application that includes three microservices, a Docker Compose file, and a Jenkins build pipeline. You can clone the GitHub repository locally and learn firsthand how the continuous delivery process with Docker works. Much of the advice and patterns in this guide also apply to several other container technologies. With this book, you'll explore: Continuous delivery basics with Java-and how JAR and WAR files differ from containers How Docker impacts a typical Java application CD build pipeline The impact of microservices and cloud-native Twelve-Factor Applications on Java architectural patterns How containers affect functional testing, and non-functional performance and security testing Host-level monitoring, container-level metrics, and application-level health checks About the author: Daniel Bryant is Chief Scientist at OpenCredo and CTO at SpectoLabs. Acting as a technical leader and aspiring to be the archetypal "generalizing specialist," his current work includes enabling agility within organizations. He does so by focusing on the relevance of architecture within agile development, implementing high-quality testing strategies, and facilitating continuous integration/delivery.
    Anmerkung: Description based on online resource; title from title page (Safari, viewed March 30, 2017)
    URL: https://learning.oreilly.com/library/view/-/9781491986851/?ar
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  • 22
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    Data science with Java : practical methods for scientists and engineers (2017)
    Sebastopol, CA : O'Reilly Media
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 volume) , illustrations
    Ausgabe: First edition.
    Schlagwort(e): Java (Computer program language) ; Machine learning ; Application software ; Development ; Electronic books ; Electronic books ; local
    Kurzfassung: Data Science is booming thanks to R and Python, but Java brings the robustness, convenience, and ability to scale critical to today's data science applications. With this practical book, Java software engineers looking to add data science skills will take a logical journey through the data science pipeline. Author Michael Brzustowicz explains the basic math theory behind each step of the data science process, as well as how to apply these concepts with Java. You'll learn the critical roles that data IO, linear algebra, statistics, data operations, learning and prediction, and Hadoop MapReduce play in the process. Throughout this book, you'll find code examples you can use in your applications. Examine methods for obtaining, cleaning, and arranging data into its purest form Understand the matrix structure that your data should take Learn basic concepts for testing the origin and validity of data Transform your data into stable and usable numerical values Understand supervised and unsupervised learning algorithms, and methods for evaluating their success Get up and running with MapReduce, using customized components suitable for data science algorithms
    Anmerkung: Includes index. - Description based on online resource; title from cover (Safari, viewed June 12, 2017)
    URL: https://learning.oreilly.com/library/view/-/9781491934104/?ar
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  • 23
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    Agile data mastering (2017)
    Sebastopol, CA : O'Reilly Media
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 volume) , illustrations
    Ausgabe: First edition.
    Schlagwort(e): Big data ; Business enterprises ; Data processing ; Information technology ; Management ; Database management ; Electronic books ; Electronic books ; local
    Kurzfassung: Organizations across all industries are attempting to capitalize on the promise of Big Data by using their information assets as a source of competitive advantage. In doing so, they are investing heavily in areas such as analytic tools and new storage capabilities. However, they often neglect the data management layer of the equation: it's not simply about finding an optimal way to store or analyze the data, but it's also vital that you prepare and manage the data for consumption. After all, if the data is inaccurate or incomplete, no consumer will trust or use it. Typically, organizations expend a lot of time on manual data cleaning and vetting to create "master records"-a single, trusted view of an organizational entity such as a customer or supplier-and this is often the area where most help is needed. This report explains just how powerful machine learning can be when applied directly to the creation of master data records. Known as agile data mastering, this method leverages ML's speed and flexibility to quickly create accurate master records that can scale across datasets and domains. You'll learn agile data mastering processes based on the operation of Tamr, an enterprise-scale data unification company that applies human-guided machine learning to this task. This report explores the: Overall importance and many uses of master data records Challenge of creating these records in distributed, complex data environments Differences between traditional master data management (MDM) and agile data mastering Advantages of agile data mastering Technology of Tamr, a data unification company that provides agile data mastering solutions
    Anmerkung: Description based on online resource; title from title page (Safari, viewed January 8, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781492028468/?ar
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  • 24
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    Creating a data-driven enterprise with DataOps : insights from Facebook, Uber, LinkedIn, Twitter, and eBay (2017)
    Sebastopol, CA : O'Reilly Media
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 volume) , illustrations
    Ausgabe: First edition.
    Schlagwort(e): Decision making ; Data processing ; Information visualization ; Big data ; Information technology ; Management ; Business enterprises ; Technological innovations ; Electronic books ; Electronic books ; local
    Kurzfassung: Many companies are busy collecting massive amounts of data, but few are taking advantage of this treasure horde to build a truly data insights-driven organization. To do so, the data team must democratize both data and the insights in a way that provides real-time access to all employees in the organization. This report explores DataOps, the process, culture, tools, and people required to scale big data pervasively across the enterprise. Just as DevOps has enabled organizations to improve coordination between developers and the operations team, DataOps closely connects everyone who handles data, including engineers, data scientists, analysts, and business users. Democratizing data with this approach requires removing barriers typical of siloed data, teams, and systems. In this report, Apache Hive creators Ashish Thusoo and Joydeep Sen Sarma examine the characteristics of a data-driven organization that supports a self-service model. Explore related topics such as data lakes, metadata, cloud architecture, and data-infrastructure-as-a-service Examine conclusions from a survey of more than 400 senior executives whose companies are in various stages of data maturity Learn how data pioneers at Facebook, Uber, LinkedIn, Twitter, and eBay created data-driven cultures and self-service data infrastructures for their organizations
    Anmerkung: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed January 3, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781492049227/?ar
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  • 25
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    Think data structures : algorithms and information retrieval in Java (2017)
    Sebastopol, CA : O'Reilly Media
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 volume) , illustrations
    Ausgabe: First edition.
    Schlagwort(e): Java (Computer program language) ; Data structures (Computer science) ; Electronic books ; Electronic books ; local
    Kurzfassung: If you're a student studying computer science or a software developer preparing for technical interviews, this practical book will help you learn and review some of the most important ideas in software engineering-data structures and algorithms-in a way that's clearer, more concise, and more engaging than other materials. By emphasizing practical knowledge and skills over theory, author Allen Downey shows you how to use data structures to implement efficient algorithms, and then analyze and measure their performance. You'll explore the important classes in the Java collections framework (JCF), how they're implemented, and how they're expected to perform. Each chapter presents hands-on exercises supported by test code online. Use data structures such as lists and maps, and understand how they work Build an application that reads Wikipedia pages, parses the contents, and navigates the resulting data tree Analyze code to predict how fast it will run and how much memory it will require Write classes that implement the Map interface, using a hash table and binary search tree Build a simple web search engine with a crawler, an indexer that stores web page contents, and a retriever that returns user query results Other books by Allen Downey include Think Java , Think Python , Think Stats , and Think Bayes .
    Anmerkung: Includes index. - Description based on online resource; title from title page (Safari, viewed July 18, 2017)
    URL: https://learning.oreilly.com/library/view/-/9781491972373/?ar
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  • 26
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    Big data now : 2016 edition : current perspectives from O'Reilly Media (2017)
    Sebastopol, CA : O'Reilly Media
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 volume) , illustrations
    Ausgabe: First edition.
    Schlagwort(e): Big data ; Data mining ; Database searching ; Electronic data processing ; Distributed processing ; File organization (Computer science) ; Electronic books ; Electronic books ; local
    Kurzfassung: A collection of blog posts written for oreilly.com organized around six key themes: careers in data; tools and architecture for big data; intelligent real-time applications; cloud infrastructure; machine learning, models and training; deep learning and AI. Cf. Introduction.
    Anmerkung: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed January 3, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781492049197/?ar
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  • 27
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    Introduction to GPUs for data analytics : advances and applications for accelerated computing (2017)
    Sebastopol, CA : O'Reilly Media
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 volume) , illustrations
    Ausgabe: First edition.
    Schlagwort(e): Graphics processing units ; Programming ; Real-time data processing ; Machine learning ; Big data ; Electronic books ; Electronic books ; local
    Kurzfassung: Moore's law has finally run out of steam for CPUs. The number of x86 cores that can be placed cost-effectively on a single chip has reached a practical limit, making higher densities prohibitively expensive for most applications. Fortunately, for big data analytics, machine learning, and database applications, a more capable and cost-effective alternative for scaling compute performance is already available: the graphics processing unit, or GPU. In this report, executives at Kinetica and Sierra Communications explain how incorporating GPUs is ideal for keeping pace with the relentless growth in streaming, complex, and large data confronting organizations today. Technology professionals, business analysts, and data scientists will learn how their organizations can begin implementing GPU-accelerated solutions either on premise or in the cloud. This report explores: How GPUs supplement CPUs to enable continued price/performance gains The many database and data analytics applications that can benefit from GPU acceleration Why GPU databases with user-defined functions (UDFs) can simplify and unify the machine learning/deep learning pipeline How GPU-accelerated databases can process streaming data from the Internet of Things and other sources in real time The performance advantage of GPU databases in demanding geospatial analytics applications How cognitive computing-the most compute-intensive application currently imaginable-is now within reach, using GPUs
    Anmerkung: Description based on online resource; title from title page (Safari, viewed January 9, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781491998045/?ar
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  • 28
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    Building reactive microservices in Java : asynchronous and event-based application design (2017)
    Sebastopol, CA : O'Reilly Media
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 volume) , illustrations
    Ausgabe: First edition.
    Schlagwort(e): Java (Computer program language) ; Web applications ; Application software ; Development ; Electronic books ; Electronic books ; local
    Kurzfassung: If you're investigating ways to build distributed microservices, perhaps to replace an unwieldy monolithic enterprise application, this report explains the benefits of creating microservices with reactive design-a method that takes advantage of modern CPU architectures and efficient resource utilization. You'll learn how to build effective microservice systems with Eclipse Vert.x, a toolkit for building reactive applications on the JVM. Clement Escoffier, Principal Software Engineer at Red Hat and a Core Developer on Vert.x, shows developers and architects how to get started with this toolkit. By learning first how to build a single microservice, and then an entire system, you'll learn the benefits of using reactive principles for building and deploying microservices that are autonomous, asynchronous, resilient, and elastic. Explore the elements of reactive microservices and learn how Vert.x works Build and consume a single microservice to understand how messaging improves its reactiveness Create an entire microservices system, using stability and resilience patterns to manage failures Use the OpenShift container platform to deploy and manage microservices in a virtual or cloud environment
    Anmerkung: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed December 6, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781491986295/?ar
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  • 29
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    Java 9 modularity : patterns and practices for developing maintainable applications (2017)
    Sebastopol, CA : O'Reilly Media
    Details
    ISBN: 9781491954164 , 1491954167
    Sprache: Englisch
    Seiten: 1 online resource (1 volume) , illustrations
    Ausgabe: First edition.
    Schlagwort(e): Java (Computer program language) ; Electronic books ; Electronic books ; local
    Kurzfassung: Covers the Java Platform Module System in Java 11 Java 9 introduced the Java Platform Module System. The introduction of the module system affects existing applications and offers new ways of creating modular and maintainable applications. With this hands-on book, Java developers will learn not only about the joys of modularity, but also about the patterns needed to create truly modular and reliable applications. Authors Sander Mak and Paul Bakker teach you the concepts behind the Java Platform Module System, along with the powerful tools it offers. You'll also learn how to modularize existing code and how to build new Java applications in a modular way. Understand the Java Platform Module System concepts Master the patterns and practices for building truly modular applications Migrate existing applications and libraries to Java modules Use JDK tools for modular development and migration
    Anmerkung: Includes index. - Description based on online resource; title from title page (Safari, viewed September 15, 2017)
    URL: https://learning.oreilly.com/library/view/-/9781491954157/?ar
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  • 30
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    Development workflows for data scientists (2017)
    Sebastopol, CA : O'Reilly Media
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 volume) , illustrations
    Ausgabe: First edition.
    Schlagwort(e): Workflow ; Management ; Computer programs ; Big data ; Computer software ; Development ; Electronic data processing ; Management ; Information visualization ; Electronic books ; Electronic books ; local
    Kurzfassung: Data science teams often borrow best practices from software development, but since the product of a data science project is insight, not code, software development workflows are not a perfect fit. How can data scientists create workflows tailored to their needs? Through interviews with several data-driven organizations, this practical report reveals how data science teams are improving the way they define, enforce, and automate a development workflow. Data science workflows differ from team to team because their tasks, goals, and skills vary so much. In this report, author Ciara Byrne talked to teams from BinaryEdge, Airbnb, GitHub, Scotiabank, Fast Forward Labs, Datascope, and others about their approaches to the data science process, including their procedures for: Defining team structure and roles Asking interesting questions Examining previous work Collecting, exploring, and modeling data Testing, documenting, and deploying code to production Communicating the results With this report, you'll also examine a complete data science workflow developed by the team from Swiss cybersecurity firm BinaryEdge that includes steps for preliminary data analysis, exploratory data analysis, knowledge discovery, and visualization.
    Anmerkung: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed January 3, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781492049319/?ar
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  • 31
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    Think Java : how to think like a computer scientist (2016)
    Sebastopol, CA : O'Reilly Media
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 volume) , illustrations
    Ausgabe: First edition.
    Schlagwort(e): Java (Computer program language) ; Computer programming ; Electronic books ; Electronic books ; local
    Kurzfassung: Currently used at many colleges, universities, and high schools, this hands-on introduction to computer science is ideal for people with little or no programming experience. The goal of this concise book is not just to teach you Java, but to help you think like a computer scientist. You'll learn how to program-a useful skill by itself-but you'll also discover how to use programming as a means to an end. Authors Allen Downey and Chris Mayfield start with the most basic concepts and gradually move into topics that are more complex, such as recursion and object-oriented programming. Each brief chapter covers the material for one week of a college course and includes exercises to help you practice what you've learned.
    Anmerkung: Includes index. - Description based on online resource; title from title page (Safari, viewed May 23, 2016)
    URL: lizenzpflichtig
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  • 32
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    Fast data architectures for streaming applications : getting answers now from data sets that never end (2016)
    Sebastopol, CA : O'Reilly Media
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 volume) , illustrations
    Ausgabe: First edition.
    Schlagwort(e): Service-oriented architecture (Computer science) ; Application software ; Development ; Data mining ; Big data ; Electronic books ; Electronic books ; local
    Kurzfassung: Why have stream-oriented data systems become so popular, when batch-oriented systems have served big data needs for many years? In this report, author Dean Wampler examines the rise of streaming systems for handling time-sensitive problems-such as detecting fraudulent financial activity as it happens. You'll explore the characteristics of fast data architectures, along with several open source tools for implementing them. Batch-mode processing isn't going away, but exclusive use of these systems is now a competitive disadvantage. You'll learn that, while fast data architectures are much harder to build, they represent the state of the art for dealing with mountains of data that require immediate attention. Learn step-by-step how a basic fast data architecture works Understand why event logs are the core abstraction for streaming architectures, while message queues are the core integration tool Use methods for analyzing infinite data sets, where you don't have all the data and never will Take a tour of open source streaming engines, and discover which ones work best for different use cases Get recommendations for making real-world streaming systems responsive, resilient, elastic, and message driven Explore an example streaming application for the IoT: telemetry ingestion and anomaly detection for home automation systems
    Anmerkung: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed February 27, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781492038771/?ar
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  • 33
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    Architecting HBase applications : a guidebook for successful development and design (2016)
    Sebastopol, CA : O'Reilly Media
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 volume) , illustrations
    Ausgabe: First edition.
    Schlagwort(e): Non-relational databases ; Big data ; Real-time data processing ; Distributed databases ; Electronic data processing ; Distributed processing ; Database management ; Open source software ; Electronic books ; Electronic books ; local
    Kurzfassung: Lots of HBase books, online HBase guides, and HBase mailing lists/forums are available if you need to know how HBase works. But if you want to take a deep dive into use cases, features, and troubleshooting, Architecting HBase Applications is the right source for you. With this book, you'll learn a controlled set of APIs that coincide with use-case examples and easily deployed use-case models, as well as sizing/best practices to help jump start your enterprise application development and deployment.
    Anmerkung: Includes bibliographical references and index. - Description based on online resource; title from title page (viewed July 22, 2016)
    URL: https://learning.oreilly.com/library/view/-/9781491915806/?ar
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  • 34
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    Reactive programming with RxJava : creating asynchronous, event-based applications (2016)
    Sebastopol, CA : O'Reilly Media
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 volume) , illustrations
    Schlagwort(e): Android (Electronic resource) ; Application software ; Development ; Java (Computer program language) ; Electronic books ; Electronic books ; local
    Kurzfassung: In today's app-driven era, when programs are asynchronous and responsiveness is so vital, reactive programming can help you write code that's more reliable, easier to scale, and better-performing. With this practical book, Java developers will first learn how to view problems in the reactive way, and then build programs that leverage the best features of this exciting new programming paradigm. Authors Tomasz Nurkiewicz and Ben Christensen include concrete examples that use the RxJava library to solve real-world performance issues on Android devices as well as the server.
    Anmerkung: Includes index. - Description based on online resource; title from title page (Safari, viewed October 13, 2016)
    URL: https://learning.oreilly.com/library/view/-/9781491931646/?ar
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    Building maintainable software : ten guidelines for future-proof code (2016)
    Sebastopol, CA : O'Reilly Media
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 volume) , illustrations
    Ausgabe: Java edition.
    Schlagwort(e): Computer software ; Development ; Computer software ; Quality control ; Java (Computer program language) ; Electronic books ; Electronic books ; local
    Kurzfassung: Have you ever felt frustrated working with someone else's code? Difficult-to-maintain source code is a big problem in software development today, leading to costly delays and defects. Be part of the solution. With this practical book, you'll learn 10 easy-to-follow guidelines for delivering Java software that's easy to maintain and adapt. These guidelines have been derived from analyzing hundreds of real-world systems. Written by consultants from the Software Improvement Group (SIG), this book provides clear and concise explanations, with advice for turning the guidelines into practice.
    Anmerkung: Includes index. - Description based on online resource; title from title page (Safari, viewed February 5, 2016)
    URL: https://learning.oreilly.com/library/view/-/9781491955987/?ar
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  • 36
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    Architecting for access : simplifying analytics on big data infrastructure (2016)
    Sebastopol, CA : O'Reilly Media
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 volume) , illustrations
    Ausgabe: First edition.
    Schlagwort(e): Big data ; Electronic data processing ; Distributed processing ; Information visualization ; Information technology ; Management ; Business intelligence ; Electronic books ; Electronic books ; local
    Kurzfassung: Fragmented, disparate backend data systems have become the norm in today's enterprise, where you'll find a mix of relational databases, Hadoop stores, and NoSQL engines, with access and analytics tools bolted on every which way. This mishmash of options presents a real challenge when it comes to choosing frontend analytics and visualization tools. How did we get here? In this O'Reilly report, IT veteran Rich Morrow takes you through the rapid changes to both backend storage and frontend analytics over the past decade, and provides a pragmatic list of requirements for an analytics stack that will centralize access to all of these data systems. You'll examine current analytics platforms, including Looker-a new breed of analytics and visualization tools built specifically to handle our fragmented data space. Understand why and how data became so fractured so quickly Explore the tangled web of data and backend tools in today's enterprises Learn the tool requirements for accessing and analyzing the full spectrum of data Examine the relative strengths of popular analytics and visualization tools, including Looker, Tableau, and MicroStrategy Inspect Looker's unique focus on both the frontend and backend
    Anmerkung: Description based on online resource; title from title page (Safari, viewed June 13, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781492042723/?ar
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    Data science for modern manufacturing : global trends : big data analytics for the industrial Internet of Things (2016)
    Sebastopol, CA : O'Reilly Media
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 volume) , illustrations
    Ausgabe: First edition.
    Schlagwort(e): Industries ; Technological innovations ; Internet of things ; Big data ; Electronic books ; Electronic books ; local
    Kurzfassung: The world's leading nations are standing at the precipice of the next great manufacturing revolution-one in which the Industrial Internet of Things (IIoT) and big data analytics are already making a major impact. In this O'Reilly report, author Li Ping Chu shares insight from industry experts and explores recent manufacturing initiatives in China, Germany, and the US to provide a succinct, hype-free overview of related technologies and applications. You'll learn what government groups are doing to promote the Industrial Internet, the technologies that are the backbone of this digital revolution, and the challenges companies whose projects are based on networked machines must consider. Learn how IIoT technology is revolutionizing the way manufacturing gathers and processes data Examine the Industrial Internet Consortium's mission to identify, assemble, and promote best practices Delve into Germany's Industrie 4.0 IIoT platform and China's government initiative Made in China 2025 Explore IIoT approaches to using technology such as Hadoop and Spark, AWS Cloud Services, GE Predix, and Siemens Sinalytics Learn about other technologies that will shape industry, including: autonomous robots, simulation, additive manufacturing, and augmented reality
    Anmerkung: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed June 13, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781492042686/?ar
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  • 38
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    Big data now : 2015 edition : current perspectives from O'Reilly Media (2016)
    Sebastopol, CA : O'Reilly Media
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 volume) , illustrations
    Ausgabe: First edition.
    Schlagwort(e): Big data ; Data mining ; Internet of things ; World Wide Web ; Security measures ; Electronic books ; Electronic books ; local
    Kurzfassung: Now in its fifth year, O'Reilly's annual Big Data Now report recaps the trends, tools, applications, and forecasts we've talked about over the past year. For 2015, we've included a collection of blog posts, authored by leading thinkers and experts in the field, that reflect a unique set of themes we've identified as gaining significant attention and traction. Our list of 2015 topics include: Data-driven cultures Data science Data pipelines Big data architecture and infrastructure The Internet of Things and real time Applications of big data Security, ethics, and governance Is your organization on the right track? Get a hold of this free report now and stay in tune with the latest significant developments in big data.
    Anmerkung: "This collection of O'Reilly blog posts, authored by leading thinkers and professionals in the field, has been grouped according to unique themes that garnered significant attention in 2015"--Introduction. - Includes bibliographical references. - Description based on online resource; title from cover (Safari, viewed March 12, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781492042273/?ar
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  • 39
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    Getting data right : tackling the challenges of big data volume and variety (2016)
    Sebastopol, CA : O'Reilly Media
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 volume) , illustrations
    Ausgabe: First edition.
    Schlagwort(e): Big data ; Web usage mining ; Data mining ; Information technology ; Management ; Electronic books ; Electronic books ; local
    Kurzfassung: Over the last 20 years, companies have invested roughly $3-4 trillion in enterprise software. These investments have been primarily focused on the development and deployment of single systems, applications, functions, and geographies targeted at the automation and optimization of key business processes. Companies are now investing heavily in big data analytics ($44 billion alone in 2014) in an effort to begin analyzing all of the data being generated from their process automation systems. But companies are quickly realizing that one of their key bottlenecks is Data Variety-the silo'd nature of the data that is a natural result of internal and external source proliferation. The problem of big data variety has crept up from the bottom-and the cost of variety is only appreciated when companies attempt to ask simple questions across many business silos (divisions, geographies, functions, etc.). Current top-down, deterministic data unification approaches (such as ETL, ELT, and MDM) were simply not designed to scale to the variety of hundreds or thousands or even tens of thousands of data silos. Download this free eBook to learn about the fundamental challenges that Data Variety poses to enterprises looking to maximize the value of their existing investments-and how new approaches promise to help organizations embrace and leverage the fundamental diversity of data. Readers will also find best practices for designing bottom-up and probabilistic methods for finding and managing data; principles for doing data science at scale in the big data era; preparing and unifying data in ways that complement existing systems; optimizing data warehousing; and how to use "data ops" to automate large-scale integration.
    Anmerkung: Description based on online resource; title from title page (Safari, viewed June 12, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781491935361/?ar
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  • 40
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    Data infrastructure for next-gen finance : tools for cloud migration, customer even hubs, governance & security (2016)
    Sebastopol, CA : O'Reilly Media
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 volume)
    Schlagwort(e): Apache Hadoop ; Financial services industry ; Technological innovations ; Cloud computing ; Big data ; Data mining ; Ubiquitous computing ; Customer relations ; Management ; Data processing ; Electronic books ; Electronic books ; local
    Kurzfassung: Stricter regulations and changing technology have forced financial services organizations to make major changes in the way they handle sensitive data. With a focus on engineering and infrastructure, this O'Reilly report examines the tools and best practices that leading financial firms are using to migrate data to the cloud, build customer event hubs, and adhere to new rules for governance and security. Based on talks given at recent Strata + Hadoop World events, this detailed report explains how Capital One, MasterCard Advisors, and the Financial Industry Regulatory Authority (FINRA) tackled major data projects with help from technology leaders such as Cloudera and Intel. Learn how FINRA migrated their portfolio from a data warehouse to the Hadoop cloud ecosystem Understand what's required to support data governance in finance, and learn about the infrastructure Capital One implemented Delve into Hadoop's security maturity model, compliance-ready security controls, and enterprise data hub for preventing breaches Examine the architecture of a Customer Event Hub, a tool that's pushing the boundaries of how organizations interact with customers
    Anmerkung: "Based on talks given at recent Strata + Hadoop World events, this detailed report explains how Capital One, MasterCard Advisors, and the Financial Industry Regulatory Authority (FINRA) tackled major data projects with help from technology leaders such as Cloudera and Intel."--Resource description page. - Description based on online resource; title from title page (Safari, viewed June 13, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781492042617/?ar
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    Modern Java EE design patterns : building scalable architecture for sustainable enterprise development (2016)
    Sebastopol, CA : O'Reilly Media
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 volume) , illustrations
    Ausgabe: First edition.
    Schlagwort(e): Java (Computer program language) ; Software patterns ; Computing platforms ; Information technology ; Management ; Electronic books ; Electronic books ; local
    Kurzfassung: With the ascent of DevOps, microservices, containers, and cloud-based development platforms, the gap between state-of-the-art solutions and the technology that enterprises typically support has greatly increased. But as Markus Eisele explains in this O'Reilly report, some enterprises are now looking to bridge that gap by building microservice-based architectures on top of Java EE. Can it be done? Is it even a good idea? Eisele thoroughly explores the possibility and provides savvy advice for enterprises that want to move ahead. The issue is complex: Java EE wasn't built with the distributed application approach in mind, but rather as one monolithic server runtime or cluster hosting many different applications. If you're part of an enterprise development team investigating the use of microservices with Java EE, this book will help you: Understand the challenges of starting a greenfield development vs tearing apart an existing brownfield application into services Examine your business domain to see if microservices would be a good fit Explore best practices for automation, high availability, data separation, and performance Align your development teams around business capabilities and responsibilities Inspect design patterns such as aggregator, proxy, pipeline, or shared resources to model service interactions Markus Eisele is a Developer Advocate at Red Hat and focuses on JBoss Middleware. He has been working with Java EE servers from different vendors for more than 14 years, and has worked with different customers on all kinds of Java EE related applications and solutions. He is a prolific blogger, writer, and tech editor for Java EE content. Markus is also a Java Champion and former ACE Director.
    Anmerkung: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed June 8, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781492042266/?ar
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    Hadoop and Spark performance for the Enterprise : ensuring quality of service in multi-tenant environments (2016)
    Sebastopol, CA : O'Reilly Media
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 volume) , illustrations
    Ausgabe: First edition.
    Schlagwort(e): Apache Hadoop ; Spark (Electronic resource : Apache Software Foundation) ; Electronic data processing ; Distributed processing ; Information technology ; Management ; Big data ; Electronic books ; Electronic books ; local
    Kurzfassung: Virtually every enterprise depends on big data analysis, but distributed computing environments such as Hadoop and Spark are complicated, to say the least. Multiple users, business units, and workload types often compete for valuable computing resources. Monitoring tools are not well equipped to handle this level of complexity, and typically provide only very high-level and historical information. The lack of fine-grained visibility for making real-time adjustments to running workloads means that high-priority jobs can easily be pushed aside by lower-priority jobs. It's time to bring Quality of Service (QoS) to distributed processing in multi-tenant Hadoop environments. This O'Reilly report explains how QoS allows operators to assign priorities to jobs, ensuring that higher-priority tasks get the resources needed to meet critical deadlines. Author Andy Oram examines the critical role of performance in the evolution of operating systems, data warehouses, and distributed processing. He also discusses Quasar (part of Mesos) and Pepperdata, two tools that can help improve performance in distributed computing environments. You'll discover how tools that help ensure QoS can help distributed environments evolve to accommodate: Multiple users contending for resources, such as those on operating systems Jobs that grow or shrink in hardware usage, so they don't strain at resource limits or let resources go to waste Jobs of different priorities, including soft real-time requirements that allow them to override lower-priority or adhoc jobs Performance guarantees, similar to service-level agreements (SLAs)
    Anmerkung: Description based on online resource; title from title page (Safari, viewed December 12, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781492048985/?ar
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    Docker for Java developers : package, deploy, and scale with ease (2016)
    Sebastopol, CA : O'Reilly Media
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 volume) , illustrations
    Ausgabe: First edition.
    Schlagwort(e): Application software ; Development ; Automation ; Java (Computer program language) ; Computing platforms ; Electronic books ; Electronic books ; local
    Kurzfassung: The established way to deploy Java applications requires you to install the Java Development Kit (JDK), plus an application server, web server, database, and other components in a data center, whether on-premise or in the cloud. Though this process works well enough, Docker containers can save you many headaches when it comes to packaging, deploying, and scaling your applications. In this O'Reilly report, author Arun Gupta explains Docker's basic concepts and the commonly used orchestration frameworks around them. You'll learn how to achieve faster startup and deployments on both Windows, Mac OS X, and Linux, and understand how these containers improve portability across machines and reduce the impedance mismatch between development, testing, and production environments. Update: Coming in Fall 2016, this report will include a chapter that explains how to load balance multiple Java application servers running as Docker containers. Learn how to provide low latency to the client by caching responses and enable health monitoring on your applications. Get up to speed on Docker basics, including its image format and toolset for building, shipping, and running containers Build and run your first Docker container by deploying a sample Java EE application with Docker Compose and Docker Swarm Manage Docker images and containers with IDEs such as NetBeans, Eclipse, and IntelliJ IDEA Use a Maven plugin to create a Docker image and start a container Ensure that user requests are appropriately distributed among servers through weighted load balancing
    Anmerkung: Description based on online resource; title from title page (Safari, viewed June 13, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781492042624/?ar
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    Architecting data lakes : data management architectures for advanced business use cases (2016)
    Sebastopol, CA : O'Reilly Media
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 volume) , illustrations
    Ausgabe: First edition.
    Schlagwort(e): Big data ; Database management ; Business enterprises ; Data processing ; Information technology ; Management ; Electronic books ; Electronic books ; local
    Kurzfassung: Many organizations use Hadoop-driven data lakes as an adjunct staging area for their enterprise data warehouses (EDW). But for those companies ready to take the plunge, a data lake is far more useful as a one-stop-shop for extracting insights from their vast collection of data. With this eBook, you'll learn best practices for building, maintaining, and deriving value from a Hadoop data lake in production environments. Authors Alice LaPlante and Ben Sharma explain how a data lake will enable your organization to manage an increasing volume of datasets-from blog postings and product reviews to streaming data-and to discover important relationships between them. Whether you want to control administrative costs in healthcare or reduce risk in financial services, this ebook addresses the architectural considerations and required capabilities you need to build your own data lake. With this report, you'll learn: The key attributes of a data lake, including its ability to store information in native formats for later processing Why implementing data management and governance in your data lake is crucial How to address various challenges for building and managing a data lake Self-service options that enable different users to access the data lake without help from IT Emerging trends that will shape the future of data lakes
    Anmerkung: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed February 11, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781492042518/?ar
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    Introduction to Apache Flink : stream processing for real time and beyond (2016)
    Sebastopol, CA : O'Reilly Media
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 volume) , illustrations
    Ausgabe: First edition.
    Schlagwort(e): Apache Flink ; Streaming technology (Telecommunications) ; Big data ; Electronic books ; Electronic books ; local
    Kurzfassung: There's growing interest in learning how to analyze streaming data in large-scale systems such as web traffic, financial transactions, machine logs, industrial sensors, and many others. But analyzing data streams at scale has been difficult to do well-until now. This practical book delivers a deep introduction to Apache Flink, a highly innovative open source stream processor with a surprising range of capabilities.
    Anmerkung: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed November 2, 2016)
    URL: https://learning.oreilly.com/library/view/-/9781491977132/?ar
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    Scaling data science for the Industrial Internet of Things : advanced analytics in real time (2016)
    Sebastopol, CA : O'Reilly Media
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 volume) , illustrations
    Ausgabe: First edition.
    Schlagwort(e): Big data ; Electronic data processing ; Distributed processing ; Management ; Information technology ; Management ; Information visualization ; Internet of things ; Electronic books ; Electronic books ; local
    Kurzfassung: One estimate holds that, by 2020, businesses involved with the Internet of Things will spend roughly 26% of their entire IoT cost on technologies and services that store, integrate, visualize, and analyze data from various IoT devices-or nearly twice what IoT companies spend today. Conventional techniques for extracting and evaluating IoT data need to get a lot smarter if companies are to keep pace with the phenomena they're tracking. In this report, author Andy Oram discusses the state of IoT analytics by examining ThingWorx and its partners Glassbeam and National Instruments-companies that provide development tools and capabilities for building and running IoT applications. By learning how to use modern techniques to automate and speed up analytics on IoT projects, you can gain an edge in security, customer satisfaction, and operational efficiencies. Understand the demands of IoT analytics by exploring examples of farm, factory, vehicle maintenance, and healthcare automation Explore the characteristics of predictive analytics, a realm where you don't know in advance which factors are relevant Examine the tools for IoT analytics used by companies at various levels of data analytics Get revealing case studies of IoT analytics from ThingWorx, Glassbeam, and National Instruments
    Anmerkung: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed January 3, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781492049265/?ar
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    Microservices for Java developers : a hands-on introduction to frameworks and containers (2016)
    Sebastopol, CA : O'Reilly Media
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 volume) , illustrations
    Ausgabe: First edition.
    Schlagwort(e): Java (Computer program language) ; Web applications ; Application software ; Development ; Electronic books ; Electronic books ; local
    Kurzfassung: Is microservice architecture right for your organization? These services have many benefits, but they also come with their own set of drawbacks. In this hands-on, example-driven guide, Java developers and architects will learn how to navigate popular application frameworks, such as Dropwizard and Spring Boot, and how to deploy and manage microservices at scale with Linux containers. Adopting microservices requires much more than changes to your technology. Author Christian Posta-a Principal Middleware Specialist/Architect at Red Hat-also examines the organizational agility necessary to deliver these services. This concise book shows you how rapid feedback cycles, autonomous teams, and shared purpose are key to making microservices work. Understand why microservices require you to think differently about building, deploying, and operating cloud-native applications Dive into the popular Spring Boot, Dropwizard, and WildFly Swarm frameworks for designing microservices Use Docker and Kubernetes to deploy microservices, regardless of language Learn cluster management, failover, and load-balancing techniques for running microservices at scale
    Anmerkung: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed June 8, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781492042228/?ar
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  • 48
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    Music science : how data and digital content are changing music (2015)
    Sebastopol, CA : O'Reilly Media
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 volume) , illustrations
    Ausgabe: First edition.
    Schlagwort(e): Music trade ; Technological innovations ; Music ; Economic aspects ; Music entrepreneurship ; Big data ; Electronic books ; Electronic books ; local
    Kurzfassung: Big data has come to the music business in a huge way. Nearly every aspect of the modern music industry relies on massive amounts of data, machine learning, and analytics to make better decisions faster. In this report, O'Reilly Strata + Hadoop World Chair Alistair Croll takes you on a tour of music science, a relatively new field staffed by data scientists, analytics experts, tastemakers, economists, and even game theorists. Based on months of research and more than 70 interviews with scientists, founders, and artists, this report provides an overview of this multibillion-dollar venture. You'll not only see where music science stands today-and where it's headed-but get a tantalizing glimpse of what other industries will look like in coming years. You'll explore: Connected listening: the new supply chain from artist to listener The rise of metadata and the sheer volume of data associated with songs Recommendations and Pandora's Music Genome Project Many ways to measure music consumption (and music consumers) Music science's Turing problems, such as the problem of predicting hit songs How algorithms on listener preference need to learn quickly with fresh data What lies on the horizon for the music industry
    Anmerkung: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed December 6, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781492048749/?ar
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    Introducing Java 8 (2015)
    Sebastopol, CA : O'Reilly Media
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 volume)
    Ausgabe: First edition.
    Schlagwort(e): Java (Computer program language) ; Application software ; Development ; Electronic books ; Electronic books ; local
    Kurzfassung: Java SE 8 is perhaps the largest change to Java in its history, led by its flagship feature-lambda expressions. If you're an experienced developer looking to adopt Java 8 at work, this short guide will walk you through all of the major changes before taking a deep dive into lambda expressions and Java 8's other big feature: the Streams API. Author Raoul-Gabriel Urma explains how improved code readability and support for multicore processors were the prime movers behind Java 8 features. He'll quickly get you up to speed on new classes including CompleteableFuture and Optional, along with enhanced interfaces and the new Date and Time API. You'll also: Understand why lambda expressions are considered a kind of anonymous function Learn how lambda expressions and the behavior parameterization pattern let you write flexible and concise code Discover various operations and data processing patterns possible when using the Streams API Use Collector recipes to write queries that are more sophisticated Consider factors such as data size and the number of cores available when using streams in parallel Work with a practical refactoring example to bring lambda expressions and streams into focus
    Anmerkung: Description based on online resource; title from title page (Safari, viewed January 7, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781492048244/?ar
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    The security data lake : leveraging big data technologies to build a common data repository for security (2015)
    Sebastopol, CA : O'Reilly Media
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 volume) , illustrations
    Ausgabe: First edition.
    Schlagwort(e): Computer networks ; Security measures ; Information visualization ; Big data ; Data mining ; Computer security ; Electronic books ; Electronic books ; local
    Kurzfassung: Companies of all sizes are considering data lakes as a way to deal with terabytes of security data that can help them conduct forensic investigations and serve as an early indicator to identify bad or relevant behavior. Many think about replacing their existing SIEM (security information and event management) systems with Hadoop running on commodity hardware. Before your company jumps into the deep end, you first need to weigh several critical factors. This O'Reilly report takes you through technological and design options for implementing a data lake. Each option not only supports your data analytics use cases, but is also accessible by processes, workflows, third-party tools, and teams across your organization. Within this report, you'll explore: Five questions to ask before choosing architecture for your backend data store How data lakes can overcome scalability and data duplication issues Different options for storing context and unstructured log data Data access use cases covering both search and analytical queries via SQL Processes necessary for ingesting data into a data lake, including parsing, enrichment, and aggregation Four methods for embedding your SIEM into a data lake
    Anmerkung: Description based on online resource; title from title page (Safari, viewed January 4, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781491927748/?ar
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    Learning Spark : lightening fast data analysis (2015)
    Sebastopol, CA : O'Reilly Media
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 volume) , illustrations
    Ausgabe: First edition.
    Schlagwort(e): ApacheSpark ; Big data ; Machine learning ; Electronic books ; Electronic books ; local
    Kurzfassung: Data in all domains is getting bigger. How can you work with it efficiently? Recently updated for Spark 1.3 , this book introduces Apache Spark, the open source cluster computing system that makes data analytics fast to write and fast to run. With Spark, you can tackle big datasets quickly through simple APIs in Python, Java, and Scala. This edition includes new information on Spark SQL, Spark Streaming, setup, and Maven coordinates.
    Anmerkung: Includes index. - Description based on online resource; title from cover page (Safari, viewed February 10, 2015)
    URL: https://learning.oreilly.com/library/view/-/9781449359034/?ar
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    Java, the legend : past, present, and future (2015)
    Sebastopol, CA : O'Reilly Media
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 volume) , illustrations
    Ausgabe: First edition.
    Schlagwort(e): Java (Computer program language) ; Electronic books ; Electronic books ; local
    Kurzfassung: "Java has inspired a lot of hatred, but it's been incredibly influential in building modern enterprise software, along with the tools we use to develop, maintain, and deploy that software." -Mike Loukides, O'Reilly Media The road from Java's first public alpha of 1.0 to today has been long-and full of technical advances, innovative solutions, and interesting complications. Along the way, Java has flourished and is now one of the world's most important and widely-used programming environments. Benjamin Evans, the Java editor for InfoQ and author of Java in a Nutshell, 6th edition, takes us on a journey through time: How Java has benefitted from early design decisions, including "Write Once, Run Anywhere" and an insistence on backward compatibility The impact of open source The enormous success and continued importance of the Java Virtual Machine and platform The rise of Enterprise Java The evolution of the Java developer community and ecosystem Java's continuing influence on new programming languages Java's greatest triumphs and most heroic failures The future of Java, including Java 9, Project Panama, Project Valhalla, and the Internet of Things
    Anmerkung: Description based on online resource; title from title page (Safari, viewed January 7, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781492048299/?ar
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  • 53
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    Educating data : using data science to improve learning, motivation, and persistence (2015)
    Sebastopol, CA : O'Reilly Media
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 volume)
    Ausgabe: First edition.
    Schlagwort(e): Educational innovations ; Educational technology ; Education ; Data processing ; Decision making ; Data processing ; Big data ; Privacy, Right of ; Electronic books ; Electronic books ; local
    Kurzfassung: While big data has already made significant advances in business and government, data analytics is also beginning to transform education. This O'Reilly report explores how the use of analytics has already helped several educational programs, such as personalized learning and massive open online courses (MOOCs), for students of all ages. Of course, that's only part of the story. As author Taylor Martin explains, researchers, educators, and private practitioners in the field have also run into several challenges in bringing the education field up to speed. Issues such as building data infrastructures, integrating data sources, and assuring student privacy still need to be resolved-as does the problem of teaching a new generation of data scientists about the challenges and opportunities unique to education. Download this report and find out what educators and analysts have accomplished so far, and how they hope data analytics will help improve outcomes for students, parents, schools, and teachers in the near future. Taylor Martin is a professor of Instructional Technology and Learning Sciences at Utah State University. She researches how people learn from active participation, both physical and social. Currently on rotation at the National Science Foundation, Dr. Martin focuses on a variety of efforts to understand how big data is impacting research in education and across the STEM disciplines.
    Anmerkung: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed January 8, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781492048107/?ar
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  • 54
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    Java in a nutshell (2015)
    Sebastopol, CA : O'Reilly Media
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 volume) , illustrations
    Ausgabe: Sixth edition.
    Schlagwort(e): Java (Computer program language) ; Web servers ; Object-oriented programming (Computer science) ; Electronic books ; Electronic books ; local
    Kurzfassung: The latest edition of Java in a Nutshell is designed to help experienced Java programmers get the most out of Java 7 and 8, but it's also a learning path for new developers. Chock full of examples that demonstrate how to take complete advantage of modern Java APIs and development best practices, this thoroughly updated book provides a fast-paced, no-fluff introduction to the Java programming language and the core runtime aspects of the Java platform.
    Anmerkung: Includes index. - Description based on online resource; title from title page (Safari, viewed March 3, 2017)
    URL: https://learning.oreilly.com/library/view/-/9781449371296/?ar
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  • 55
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    Data and social good : using data science to improve lives, fight injustice, and support democracy (2015)
    Sebastopol, CA : O'Reilly Media
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 volume)
    Ausgabe: First edition.
    Schlagwort(e): Electronic data processing ; Moral and ethical aspects ; Machine learning ; Big data ; Humanitarianism ; Electronic books ; Electronic books ; local
    Kurzfassung: Data may indeed be the "new oil"-a seemingly inexhaustible source of fuel for spectacular economic growth-but it's also a valuable resource for humanitarian groups looking to improve and protect the lives of less fortunate people. In this O'Reilly report, you'll learn how statisticians and data scientists are volunteering their time to help a variety of nonprofit organizations around the world. Mike Barlow cites several examples of how data and the work of data scientists have made a measurable impact on organizations such as DataKind, a group that connects socially minded data scientists with organizations working to address critical humanitarian issues. There's certainly no lack of demand for data science services among nonprofits today, because these organizations, too, realize the potential of data for changing people's fortunes.
    Anmerkung: Description based on online resource; title from title page (Safari, viewed January 7, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781492048213/?ar
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    The last mile of analytics (2015)
    Sebastopol, CA : O'Reilly Media
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    Sprache: Englisch
    Seiten: 1 online resource (1 volume) , illustrations
    Schlagwort(e): Decision making ; Data processing ; Machine learning ; Big data ; Electronic books ; Electronic books ; local
    Kurzfassung: Here's the net takeaway: Businesses want insights from data they can translate into meaningful actions and real results. Software vendors are beginning to deliver a new generation of advanced analytics packages that address business issues directly. In this O'Reilly report, Mike Barlow reveals how this new user-friendly software is helping businesses go beyond data analysis and straight to decision-making-without requiring data science expertise or truckloads of cash. How has advanced analytics progressed from lab project to commercial product so quickly? Through interviews with data analysts, you'll understand the role that machine learning plays in specialized analytics packages, and how this software alone can make decisions based on what's likely to happen next. When you have these capabilities, you've reached "the last mile of analytics."
    Anmerkung: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed January 7, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781492048084/?ar
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    Big data now : 2014 edition : current perspectives from O'Reilly Media (2015)
    Sebastopol, CA : O'Reilly Media
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 volume) , illustrations
    Ausgabe: First edition.
    Schlagwort(e): Big data ; Data mining ; Artificial intelligence ; Information technology ; Management ; Electronic books ; Electronic books ; local
    Kurzfassung: In the four years that O'Reilly has produced its annual Big Data Now report, the data field has grown from infancy into young adulthood. Data is now a leader in some fields and a driver of innovation in others, and companies that use data and analytics to drive decision-making are outperforming their peers. And while access to big data tools and techniques once required significant expertise, today many tools have improved and communities have formed to share best practices. Companies have also started to emphasize the importance of processes, culture, and people. The topics in this 2014 edition of Big Data Now represent the major forces currently shaping the data world: Cognitive augmentation: predictive APIs, graph analytics, and Network Science dashboards Intelligence matters: defining AI, modeling intelligence, deep learning, and "summoning the demon" Cheap sensors, fast networks, and distributed computing: stream processing, hardware data flows, and computing at the edge Data (science) pipelines: broadening the coverage of analytic pipelines with specialized tools Evolving marketplace of big data components: SSDs, Hadoop 2, Spark; and why datacenters need operating systems Design and social science: human-centered design, wearables and real-time communications, and wearable etiquette Building a data culture: moving from prediction to real-time adaptation; and why you need to become a data skeptic Perils of big data: data redlining, intrusive data analysis, and the state of big data ethics
    Anmerkung: "The topics in this 2014 edition of Big Data Now represent the major forces currently shaping the data world"--Resource description page. - Includes bibliographical references. - Description based on online resource; title from cover (Safari, viewed March 12, 2019)
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    Data analytics in sports : how playing with data transforms the game (2015)
    Sebastopol, CA : O'Reilly Media
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    Sprache: Englisch
    Seiten: 1 online resource (1 volume) , illustrations
    Ausgabe: First edition.
    Schlagwort(e): Sports ; Data processing ; Sports ; Economic aspects ; Big data ; Information visualization ; Electronic books ; Electronic books ; local
    Kurzfassung: As any child with a baseball card intuitively knows, sports and statistics go hand-in-hand. Yet, the general media disdain the flood of sports statistics available today: sports are pure and analytic tools are not. Well, if the so-called purists find tools like baseball's sabermetrics upsetting, then they'd better brace themselves for the new wave of data analytics. In this O'Reilly report, Janine Barlow examines how advanced predictive analytics are impacting the world of sports-from the rise of tools such as Major League Baseball's Statcast, which collects data on the movement of balls and players, to SportVU, which the National Basketball Association uses to collect spatial analysis data. You'll also learn: How "Dance Card" makes accurate predictions about NCAA's "March Madness" tournament Why data is crumbling long-standing myths about performance in soccer How the National Football League is using wearable devices to collect vital health data about its players It's a new world in sports, where data analytics and related information technologies are changing the experience for teams, players, fans, and investors.
    Anmerkung: Description based on online resource; title from title page (Safari, viewed January 4, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781491927496/?ar
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    RxJava for Android app development (2015)
    Sebastopol, CA : O'Reilly Media
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 volume) , illustrations
    Ausgabe: First edition.
    Schlagwort(e): Android (Electronic resource) ; Application software ; Development ; Java (Computer program language) ; Web applications ; Mobile computing ; Electronic books ; Electronic books ; local
    Kurzfassung: RxJava is the "new hotness" for Android development. However, there are still many developers that aren't really sure what RxJava is or how it might help them build their Android applications. This report will leave developers with a solid understanding of RxJava and help them understand why so many developers are excited about RxJava for Android development.
    Anmerkung: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed January 8, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781492048343/?ar
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  • 60
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    Oil, gas, and data : high-performance data tools in the production of industrial power (2015)
    Sebastopol, CA : O'Reilly Media
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 volume)
    Ausgabe: First edition.
    Schlagwort(e): Petroleum industry and trade ; Statistics ; Gas industry ; Statistics ; Big data ; Machine learning ; Electronic books ; Electronic books ; local
    Kurzfassung: Oil and gas companies have been dealing with large amounts of data much longer than most industries, and some energy analysts even refer to it as the "original big data industry." Now, with massive increases of seismic data, advances in network-attached devices, and a vast quantity of historical data on paper, the oil and gas space also presents one of today's most complex data science problems. As this O'Reilly report reveals, the industry is working to add machine learning and predictive analytics in all phases of its exploration, production, refinement, and delivery operations. But it's still in the early adoption phase. While oil and gas has embraced the 'digital oilfield' concept, it's a cautious IT culture, with many companies waiting to see what others do first. In this report, you'll learn how: Big data solutions from other industries can't be easily applied to oil and gas Much innovation is in the discovery and exploration phase, where risk and uncertainty are high Outside companies such as Hortonworks, SparkBeyond, and WellWiki are making a difference Oil companies now run some of the largest private supercomputing facilities in the world Security tools such as rapid detection are important to an industry with memories of the Stuxnet worm and Shamoon virus
    Anmerkung: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed January 7, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781492048206/?ar
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    Minecraft modding with Forge (2015)
    Sebastopol, CA : O'Reilly Media
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    Sprache: Englisch
    Seiten: 1 online resource (1 volume) , illustrations
    Ausgabe: First edition.
    Paralleltitel: Erscheint auch als
    Schlagwort(e): Computer games ; Programming ; Minecraft (Game) ; Java (Computer program language) ; Electronic books ; Electronic books ; local
    Kurzfassung: Playing Minecraft is a lot of fun, but the game is more engaging, entertaining, and educational when kids learn how to build mods-small programs that let them modify game elements and add content. This family-friendly guide teaches kids and parents how to create mods of different types, using the Minecraft Forge modding tool. No programming experience is needed. You'll not only build some amazing mods with the book's easy-to-follow instructions, but you'll also learn how to work with Java, the same programming language that Minecraft uses. Why wait? Get started with computer programming and be more creative with Minecraft while you're at it! This guide is based on workshops the authors deliver to kids around the world.
    Anmerkung: Includes index. - Description based on print version record
    URL: https://learning.oreilly.com/library/view/-/9781491918883/?ar
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    Big data for chimps : a guide to massive-scale data processing in practice (2015)
    Sebastopol, CA : O'Reilly Media
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 volume) , illustrations
    Ausgabe: First edition.
    Schlagwort(e): Apache Hadoop ; MapReduce (Computer file) ; Big data ; Data mining ; Electronic data processing ; Electronic books ; Electronic books ; local
    Kurzfassung: Finding patterns in massive event streams can be difficult, but learning how to find them doesn't have to be. This unique hands-on guide shows you how to solve this and many other problems in large-scale data processing with simple, fun, and elegant tools that leverage Apache Hadoop. You'll gain a practical, actionable view of big data by working with real data and real problems. Perfect for beginners, this book's approach will also appeal to experienced practitioners who want to brush up on their skills. Part I explains how Hadoop and MapReduce work, while Part II covers many analytic patterns you can use to process any data. As you work through several exercises, you'll also learn how to use Apache Pig to process data.
    Anmerkung: Includes index. - Description based on online resource; title from cover page (Safari, viewed October 12, 2015)
    URL: https://learning.oreilly.com/library/view/-/9781491923931/?ar
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    Advanced analytics with Spark (2015)
    Sebastopol, CA : O'Reilly Media
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 volume) , illustrations
    Ausgabe: First edition.
    Paralleltitel: Erscheint auch als
    Schlagwort(e): Spark (Electronic resource : Apache Software Foundation) ; Big data ; Data mining ; Computer programs ; Electronic books ; Electronic books ; local
    Kurzfassung: In this practical book, four Cloudera data scientists present a set of self-contained patterns for performing large-scale data analysis with Spark. The authors bring Spark, statistical methods, and real-world data sets together to teach you how to approach analytics problems by example. You'll start with an introduction to Spark and its ecosystem, and then dive into patterns that apply common techniques-classification, collaborative filtering, and anomaly detection among others-to fields such as genomics, security, and finance. If you have an entry-level understanding of machine learning and statistics, and you program in Java, Python, or Scala, you'll find these patterns useful for working on your own data applications.
    Anmerkung: Includes index. - Description based on print version record
    URL: https://learning.oreilly.com/library/view/-/9781491912751/?ar
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    Managing the data lake : moving to big data analysis (2015)
    Sebastopol, CA : O'Reilly Media
    Details
    ISBN: 9781491941683 , 1491941685
    Sprache: Englisch
    Seiten: 1 online resource (1 volume)
    Ausgabe: First edition.
    Schlagwort(e): Big data ; Quantitative research ; Database management ; Electronic books ; Electronic books ; local
    Kurzfassung: Organizations across many industries have recently created fast-growing repositories to deal with an influx of new data from many sources and often in multiple formats. To manage these data lakes, companies have begun to leave the familiar confines of relational databases and data warehouses for Hadoop and various big data solutions. But adopting new technology alone won't solve the problem. Based on interviews with several experts in data management, author Andy Oram provides an in-depth look at common issues you're likely to encounter as you consider how to manage business data. You'll explore five key topic areas, including: Acquisition and ingestion: how to solve these problems with a degree of automation. Metadata: how to keep track of when data came in and how it was formatted, and how to make it available at later stages of processing. Data preparation and cleaning: what you need to know before you prepare and clean your data, and what needs to be cleaned up and how. Organizing workflows: what you should do to combine your tasks-ingestion, cataloging, and data preparation-into an end-to-end workflow. Access control: how to address security and access controls at all stages of data handling. Andy Oram, an editor at O'Reilly Media since 1992, currently specializes in programming. His work for O'Reilly includes the first books on Linux ever published commercially in the United States.
    Anmerkung: Description based on online resource; title from title page (viewed January 4, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781492049876/?ar
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    Data science at the command line : facing the future with time-tested tools (2015)
    Sebastopol, CA : O'Reilly Media
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    Sprache: Englisch
    Seiten: 1 online resource (1 v.) , ill.
    Schlagwort(e): Linux ; Scripting languages (Computer science) ; Big data ; Data mining ; Information science ; Electronic books ; Electronic books ; local
    Kurzfassung: This hands-on guide demonstrates how the flexibility of the command line can help you become a more efficient and productive data scientist. You'll learn how to combine small, yet powerful, command-line tools to quickly obtain, scrub, explore, and model your data. To get you started, author Jeroen Janssens introduces the Data Science Toolbox, an easy-to-install virtual environment packed with over 80 command-line tools.
    Anmerkung: Includes bibliographical references. - Description based on online resource; title from cover (Safari, viewed Oct. 10, 2014)
    URL: lizenzpflichtig
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    Java cookbook (2014)
    Sebastopol, CA : O'Reilly Media
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    Sprache: Englisch
    Seiten: 1 online resource (1 v.) , ill.
    Ausgabe: 3rd ed.
    Schlagwort(e): Java (Computer program language) ; Electronic books ; Electronic books ; local
    Kurzfassung: From lambda expressions and JavaFX 8 to new support for network programming and mobile development, Java 8 brings a wealth of changes. This cookbook helps you get up to speed right away with hundreds of hands-on recipes across a broad range of Java topics. You'll learn useful techniques for everything from debugging and data structures to GUI development and functional programming.
    Anmerkung: Includes index. - Description based on online resource; title from title page (Safari, viewed July 14, 2014)
    URL: https://learning.oreilly.com/library/view/-/9781449338794/?ar
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    Doing data science (2014)
    Sebastopol, CA : O'Reilly Media
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    Sprache: Englisch
    Seiten: 1 online resource (1 v.) , ill.
    Schlagwort(e): Big data ; Data mining ; Information science ; Data structures (Computer science) ; Information visualization ; Electronic books ; Electronic books ; local
    Kurzfassung: Now that people are aware that data can make the difference in an election or a business model, data science as an occupation is gaining ground. But how can you get started working in a wide-ranging, interdisciplinary field that's so clouded in hype? This insightful book, based on Columbia University's Introduction to Data Science class, tells you what you need to know. In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you're familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science. Topics include: Statistical inference, exploratory data analysis, and the data science process Algorithms Spam filters, Naive Bayes, and data wrangling Logistic regression Financial modeling Recommendation engines and causality Data visualization Social networks and data journalism Data engineering, MapReduce, Pregel, and Hadoop Doing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O'Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course.
    Anmerkung: Includes index. - Description based on online resource; title from title page (Safari, viewed Dec. 17, 2013)
    URL: https://learning.oreilly.com/library/view/-/9781449363871/?ar
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    Programming Elastic MapReduce (2014)
    Sebastopol, CA : O'Reilly Media
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    Sprache: Englisch
    Seiten: 1 online resource (1 v.) , ill.
    Schlagwort(e): Apache Hadoop ; Electronic data processing ; Distributed processing ; Big data ; Web services ; Internet programming ; Electronic books ; Electronic books ; local
    Kurzfassung: Although you don't need a large computing infrastructure to process massive amounts of data with Apache Hadoop, it can still be difficult to get started. This practical guide shows you how to quickly launch data analysis projects in the cloud by using Amazon Elastic MapReduce (EMR), the hosted Hadoop framework in Amazon Web Services (AWS).
    Anmerkung: Includes index. - Description based on online resource; title from title page (Safari, viewed Jan. 30, 2014)
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    Just hibernate : a lightweight introduction to hibernate framework (2014)
    Sebastopol, CA : O'Reilly Media
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    Sprache: Englisch
    Seiten: 1 online resource (1 v.)
    Ausgabe: 1st ed.
    Schlagwort(e): Hibernate (Electronic resource) ; Java (Computer program language) ; Web applications ; Electronic books ; Electronic books ; local
    Kurzfassung: If you're looking for a short, sweet, and simple introduction (or reintroduction) to Hibernate, this is the book you want. Through clear real-world examples, you'll learn Hibernate and object-relational mapping from the ground up, starting with the basics. Then you'll dive into the framework's moving parts to understand how they work in action.
    Anmerkung: Includes index. - Description based on online resource; title from cover (Safari, viewed June 16, 2014)
    URL: https://learning.oreilly.com/library/view/-/9781449334369/?ar
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    Becoming functional (2014)
    Sebastopol, CA : O'Reilly Media
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    Sprache: Englisch
    Seiten: 1 online resource (1 v.) , ill.
    Schlagwort(e): Functional programming (Computer science) ; Java (Computer program language) ; Electronic books ; Electronic books ; local
    Kurzfassung: If you have an imperative (and probably object-oriented) programming background, this hands-on book will guide you through the alien world of functional programming. Author Joshua Backfield begins slowly by showing you how to apply the most useful implementation concepts before taking you further into functional-style concepts and practices.
    Anmerkung: "Steps for transforming into a functional programmer"--Cover. - Includes index. - Description based on online resource; title from title page (Safari, viewed July 14, 2014)
    URL: https://learning.oreilly.com/library/view/-/9781491947487/?ar
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    Java EE 7 essentials (2013)
    Sebastopol, CA : O'Reilly Media
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    Sprache: Englisch
    Seiten: 1 online resource (1 v.) , ill.
    Paralleltitel: Erscheint auch als
    Schlagwort(e): Microsoft .NET ; Java (Computer program language) ; Electronic books ; Electronic books ; local
    Kurzfassung: Get up to speed on the principal technologies in the Java Platform, Enterprise Edition 7, and learn how the latest version embraces HTML5, focuses on higher productivity, and provides functionality to meet enterprise demands. Written by Arun Gupta, a key member of the Java EE team, this book provides a chapter-by-chapter survey of several Java EE 7 specifications, including WebSockets, Batch Processing, RESTful Web Services, and Java Message Service. You'll also get self-paced instructions for building an end-to-end application with many of the technologies described in the book, which will help you understand the design patterns vital to Java EE development. Understand the key components of the Java EE platform, with easy-to-understand explanations and extensive code samples Examine all the new components that have been added to Java EE 7 platform, such as WebSockets, JSON, Batch, and Concurrency Learn about RESTful Web Services, SOAP XML-based messaging protocol, and Java Message Service Explore Enterprise JavaBeans, Contexts and Dependency Injection, and the Java Persistence API Discover how different components were updated from Java EE 6 to Java EE 7
    Anmerkung: "Enterprise developer handbook"--Cover. - Includes index. - Description based on print version record
    URL: https://learning.oreilly.com/library/view/-/9781449370589/?ar
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  • 72
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    Online-Ressource
    Programming Grails (2013)
    Sebastopol, CA : O'Reilly Media
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 v.) , ill.
    Paralleltitel: Erscheint auch als
    Schlagwort(e): Groovy (Computer program language) ; Web site development ; Application software ; Development ; Java (Computer program language) ; Electronic books ; Electronic books ; local
    Kurzfassung: Dig deeper into Grails architecture and discover how this application framework works its magic. Written by a core developer on the Grails team, this practical guide takes you behind the curtain to reveal the inner workings of its 2.0 feature set. You'll learn best practices for building and deploying Grails applications, including performance, security, scaling, tuning, debugging, and monitoring. Understand how Grails integrates with Groovy, Spring, Hibernate, and other JVM technologies, and learn how to create and use plugins to augment your application's functionality. Once you know how Grails adds behavior by convention, you can solve problems more easily and develop applications more intuitively. Write simpler, more powerful code with the Groovy language Manage persistence in Grails, using Hibernate or a NoSQL datastore Learn how Grails uses Spring's functionality and optional modules Discover how Hibernate handles details for storing and retrieving data Integrate technologies for messaging, mail, creating web services, and other JEE technologies Bypass convention and configure Grails manually Learn a general approach to upgrading applications and plugins Use Grails to develop and deploy IaaS and PaaS applications
    Anmerkung: "Best practices for experienced Grails developers"--Cover. - Includes index. - Description based on print version record
    URL: https://learning.oreilly.com/library/view/-/9781449324513/?ar
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  • 73
    Online-Ressource
    Online-Ressource
    RESTful Java with JAX-RS 2.0 (2013)
    Sebastopol, CA : O'Reilly Media
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 v.) , ill.
    Ausgabe: 2nd ed.
    Schlagwort(e): Web site development ; Web services ; Java (Computer program language) ; Electronic books ; Electronic books ; local
    Kurzfassung: Learn how to design and develop distributed web services in Java, using RESTful architectural principles and the JAX-RS 2.0 specification in Java EE 7. By focusing on implementation rather than theory, this hands-on reference demonstrates how easy it is to get started with services based on the REST architecture. With the book's technical guide, you'll learn how REST and JAX-RS work and when to use them. The RESTEasy workbook that follows provides step-by-step instructions for installing, configuring, and running several working JAX-RS examples, using the JBoss RESTEasy implementation of JAX-RS 2.0. Learn JAX-RS 2.0 features, including a client API, server-side asynchronous HTTP, and filters and interceptors Examine the design of a distributed RESTful interface for an e-commerce order entry system Use the JAX-RS Response object to return complex responses to your client (ResponseBuilder) Increase the performance of your services by leveraging HTTP caching protocols Deploy and integrate web services within Java EE7, servlet containers, EJB, Spring, and JPA Learn popular mechanisms to perform authentication on the Web, including client-side SSL and OAuth 2.0
    Anmerkung: Includes index. - Description based on online resource; title from title page (Safari, viewed Jan. 17, 2014)
    URL: https://learning.oreilly.com/library/view/-/9781449361433/?ar
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  • 74
    Online-Ressource
    Online-Ressource
    Building mobile applications with Java (2012)
    Sebastopol, CA : O'Reilly Media
    Details
    Sprache: Englisch
    Seiten: 1 online resource (viii, 72 p.) , ill.
    Paralleltitel: Erscheint auch als
    Schlagwort(e): Google Web toolkit ; Java (Computer program language) ; Mobile computing ; Programming ; Cross-platform software development ; Application software ; Development ; Electronic books ; Electronic books ; local
    Kurzfassung: Do you want to develop mobile apps with Java-and have them work on a variety of devices powered by iOS and Android? You've come to the right place. This project-driven book shows you how to build portable apps with two amazing open source frameworks, Google Web Tools (GWT) and PhoneGap. With these tools, you'll use learn how to write Java code that compiles into cross-platform Javascript and HTML, and discover how to take advantage of features in several popular devices, such as the camera, accelerometer, and GPS. Get started with GWT by building an example Twitter search app Build a example web app and adapt it for mobile with CSS Add touch centric controls with the GWT Mobile UI library Develop a working wine journal app that tracks a user's GPS location Use techniques to make a mobile version of your web or desktop app Work with HTML5 Canvas to build a mobile video game Package your apps for iOS, webOS, and Android with PhoneGap
    Anmerkung: "Using the Google Web Toolkit and PhoneGap"--Cover. - Description based on print version record
    URL: https://learning.oreilly.com/library/view/-/9781449312718/?ar
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  • 75
    Online-Ressource
    Online-Ressource
    Java EE 6 pocket guide (2012)
    Sebastopol, CA : O'Reilly Media
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 v.) , ill.
    Paralleltitel: Erscheint auch als
    Schlagwort(e): Application software ; Development ; Web applications ; Development ; Java (Computer program language) ; Application program interfaces (Computer software) ; Electronic books ; Electronic books ; local
    Kurzfassung: This handy guide provides an overview of Java Enterprise Edition 6's main technologies and includes extensive, easy-to-understand code samples that demonstrate the platform's many improvements. You'll quickly understand how Java EE 6 simplifies the process of developing and deploying web and enterprise applications.
    Anmerkung: Includes bibliographical references and index. - Description based on print version record
    URL: https://learning.oreilly.com/library/view/-/9781449338329/?ar
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  • 76
    Online-Ressource
    Online-Ressource
    Google Script : enterprise application essentials (2012)
    Sebastopol, CA : O'Reilly Media
    Details
    Sprache: Englisch
    Seiten: 1 online resource (x, 199 p.) , ill.
    Schlagwort(e): Google Apps ; Web site development ; Software ; Application software ; Java (Computer program language) ; Electronic books ; Electronic books ; local
    Kurzfassung: How can you extend Google Apps to fit your organization's needs? This concise guide shows you how to use Google Scripts, the JavaScript-based language that provides a complete web-based development platform-with no downloads, configuration, or compiling required. You'll learn how to add functionality to Gmail, spreadsheets, and other Google services, or build data-driven apps that run from a spreadsheet, in a browser window, or within a Google Site. If you have some JavaScript experience, getting started with Google Scripts is easy. Through code examples and step-by-step instructions, you'll learn how to build applications that authenticate users, display custom data from a spreadsheet, send emails, and many more tasks. Learn Google Script's built-in debugger, script manager, and other features Create a user interface as a pop-up window, a web page, or a Google Sites gadget Use data objects and CSS to build effective product pages Automatically generate web forms from key values you specify in your Google Docs Create a database UI that works as a mobile app and Google Site gadget Use Google Docs and Gmail to create a document revision workflow
    Anmerkung: "Adding functionality to your Google Apps"--Cover
    URL: https://learning.oreilly.com/library/view/-/9781449325251/?ar
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  • 77
    Online-Ressource
    Online-Ressource
    Just Spring data access (2012)
    Sebastopol, CA : O'Reilly Media
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 v.) , ill.
    Paralleltitel: Erscheint auch als
    Schlagwort(e): Hibernate (Electronic resource) ; Java (Computer program language) ; Application software ; Development ; Electronic books ; Electronic books ; local
    Kurzfassung: JDBC has simplified database access in Java applications, but a few nagging wrinkles remain-namely, persisting Java objects to relational databases. With this book, you'll learn how the Spring Framework makes that job incredibly easy with dependency injection, template classes, and object-relational-mapping (ORM). Discover how Spring streamlines the use of JDBC and ORM tools such as Hibernate, the Java Persistence API (JPA), and Java Data Objects (JDO).
    Anmerkung: Description based on print version record
    URL: https://learning.oreilly.com/library/view/-/9781449339845/?ar
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