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  • 1
    ISBN: 9781784397401 , 1784397407
    Language: English
    Pages: 1 online resource (1 volume) , illustrations.
    Series Statement: Community experience distilled
    Keywords: Big data ; Data mining ; Electronic books ; Electronic books ; local
    Abstract: Design, process, and analyze large sets of complex data in real time About This Book Get acquainted with transformations and database-level interactions, and ensure the reliability of messages processed using Storm Implement strategies to solve the challenges of real-time data processing Load datasets, build queries, and make recommendations using Spark SQL Who This Book Is For If you are a Big Data architect, developer, or a programmer who wants to develop applications/frameworks to implement real-time analytics using open source technologies, then this book is for you. What You Will Learn Explore big data technologies and frameworks Work through practical challenges and use cases of real-time analytics versus batch analytics Develop real-word use cases for processing and analyzing data in real-time using the programming paradigm of Apache Storm Develop real-word use cases for processing and analyzing data in real-time using the programming paradigm of Apache Storm Optimize and tune Apache Storm for varied workloads and production deployments Process and stream data with Amazon Kinesis and Elastic MapReduce Perform interactive and exploratory data analytics using Spark SQL Develop common enterprise architectures/applications for real-time and batch analytics In Detail Enterprise has been striving hard to deal with the challenges of data arriving in real time or near real time. Although there are technologies such as Storm and Spark (and many more) that solve the challenges of real-time data, using the appropriate technology/framework for the right business use case is the key to success. This book provides you with the skills required to quickly design, implement and deploy your real-time analytics using real-world examples of big data use cases. Although there are technologies such as Storm and Spark (and many more) that solve the challenges of real-time data, using the appropriate technology/framework for the right business use case is the key to success. This book provides you with the skills required to quickly design, implement and deploy your real-time analytics using real-world examples of big data use cases. Moving on, we'll familiarize you with ?Amazon Kinesis? for real-time data processing on cloud. We will further develop your understanding of real-time analytics through a comprehensive review of Apache Spark along with the high-level architecture and the building blocks of a Spark program. You will learn how to transform your data...
    Note: Includes index. - Description based on online resource; title from cover page (Safari, viewed March 23, 2016)
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  • 2
    ISBN: 9781800207653
    Language: English
    Pages: 1 online resource (822 pages) , illustrations
    Edition: [First edition].
    DDC: 006.6/8
    Keywords: Tableau (Computer file) ; Information visualization Computer programs ; Visual analytics Data processing ; Electronic books ; Electronic books
    Abstract: Learning Tableau has never been easier, thanks to this practical introduction to storytelling with data. The Tableau Workshop breaks down the analytical process into five steps: data preparation, data exploration, data analysis, interactivity, and distribution of dashboards. Each stage is addressed with a clear walkthrough of the key tools and techniques you'll need, as well as engaging real-world examples, meaningful data, and practical exercises to give you valuable hands-on experience. As you work through the book, you'll learn Tableau step by step, studying how to clean, shape, and combine data, as well as how to choose the most suitable charts for any given scenario. You'll load data from various sources and formats, perform data engineering to create new data that delivers deeper insights, and create interactive dashboards that engage end-users. All concepts are introduced with clear, simple explanations and demonstrated through realistic example scenarios. You'll simulate real-world data science projects with use cases such as traffic violations, urban populations, coffee store sales, and air travel delays. By the end of this Tableau book, you'll have the skills and knowledge to confidently present analytical results and make data-driven decisions.
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  • 3
    ISBN: 9781783983995 , 178398399X
    Language: English
    Pages: 1 online resource (1 volume) , illustrations.
    Series Statement: Community experience distilled
    Keywords: Application software ; Development ; Web applications ; Python (Computer program language) ; Electronic books ; Electronic books ; local
    Abstract: Develop exciting real-world Python-based web applications with Neo4j using frameworks such as Flask, Py2neo, and Django In Detail Py2neo is a simple and pragmatic Python library that provides access to the popular graph database Neo4j via its RESTful web service interface. This brings with it a heavily refactored core, a cleaner API, better performance, and some new idioms. You will begin with licensing and installing Neo4j, learning the fundamentals of Cypher as a graph query language, and exploring Cypher optimizations. You will discover how to integrate with various Python frameworks such as Flask and its extensions: Py2neo, Neomodel, and Django. Finally, the deployment aspects of your Python-based Neo4j applications in a production environment is also covered. By sequentially working through the steps in each chapter, you will quickly learn and master the various implementation details and integrations of Python and Neo4j, helping you to develop your use cases more quickly. What You Will Learn Understand the licensing and installation of the Neo4j database and work with its various tools and utilities Learn the intricacies of Cypher as a graph query language Work with Cypher to create and modify graph data models Integrate Python and Neo4j using Py2neo Develop REST-based services over social network data using Flask and object graph models over Neo4j Integrate Django-based web applications over graph data models using Neomodel Explore different deployment models and their applicability with existing applications
    Note: Includes index. - Description based on online resource; title from cover (Safari, viewed August 3, 2015)
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