Practical Real-Time Data Processing and Analytics : A Practical Guide to Help You Tackle Different Real-Time Data Processing and Analytics Problems Using the Best Tools for Each Scenario.

Intro -- Practical Real-Time Data Processing and Analytics -- Credits -- About the Authors -- About the Reviewers -- www.PacktPub.com -- Why subscribe? -- Customer Feedback -- Table of Contents -- Preface -- What this book covers -- What you need for this book -- Who this book is for -- Conventions -- Reader feedback -- Customer support -- 1 Introducing Real-Time Analytics -- What is big data? -- Big data infrastructure -- Real-time analytics - the myth and the reality -- Near real-time solution - an architecture that works -- Lambda architecture - analytics possibilities -- IOT - thoughts and possibilities -- Cloud - considerationos for NRT and IOT -- Summary -- 2 Real Time Applications - The Basic Ingredients -- The NRT system and its building blocks -- NRT - high-level system view -- NRT - technology view -- Summary -- 3 Understanding and Tailing Data Streams -- Understanding data streams -- Setting up infrastructure for data ingestion -- Taping data from source to the processor - expectations and caveats -- Comparing and choosing what works best for your use case -- Do it yourself -- Summary -- 4 Setting up the Infrastructure for Storm -- Overview of Storm -- Storm architecture and its components -- Setting up and configuring Storm -- Real-time processing job on Storm -- Summary -- 5 Configuring Apache Spark and Flink -- Setting up and a quick execution of Spark -- Setting up and a quick execution of Flink -- Setting up and a quick execution of Apache Beam -- Balancing in Apache Beam -- Summary -- 6 Integrating Storm with a Data Source -- RabbitMQ - messaging that works -- RabbitMQ exchanges -- RabbitMQ - integration with Storm -- PubNub data stream publisher -- String together Storm-RMQ-PubNub sensor data topology -- Summary -- 7 From Storm to Sink -- Setting up and configuring Cassandra -- Storm and Cassandra topology.

Storm and IMDB integration for dimensional data -- Integrating the presentation layer with Storm -- Do It Yourself -- Summary -- 8 Storm Trident -- State retention and the need for Trident -- Basic Storm Trident topology -- Trident internals -- Trident operations -- DRPC -- Do It Yourself -- Summary -- 9 Working with Spark -- Spark overview -- Distinct advantages of Spark -- Spark - use cases -- Spark architecture - working inside the engine -- Spark pragmatic concepts -- Spark 2.x - advent of data frames and datasets -- Summary -- 10 Working with Spark Operations -- Spark - packaging and API -- RDD pragmatic exploration -- Shared variables - broadcast variables and accumulators -- Summary -- 11 Spark Streaming -- Spark Streaming concepts -- Spark Streaming - introduction and architecture -- Packaging structure of Spark Streaming -- Connecting Kafka to Spark Streaming -- Summary -- 12 Working with Apache Flink -- Flink architecture and execution engine -- Flink basic components and processes -- Integration of source stream to Flink -- Flink processing and computation -- Flink persistence -- FlinkCEP -- Pattern API -- Gelly -- DIY -- Summary -- 13 Case Study -- Introduction -- Data modeling -- Tools and frameworks -- Setting up the infrastructure -- Implementing the case study -- Running the case study -- Summary -- Index.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.