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Introduction to Apache Flink (Stream Processing for Real Time and Beyond)

List Price: $24.99
SKU:
9781491976586
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  • Product Details

    Author:
    Ellen Friedman, Kostas Tzoumas
    Format:
    Paperback
    Pages:
    107
    Publisher:
    O'Reilly Media (November 29, 2016)
    Language:
    English
    ISBN-13:
    9781491976586
    ISBN-10:
    1491976586
    Dimensions:
    6" x 9"
    File:
    TWO RIVERS-PERSEUS-Metadata_Only_Perseus_Distribution_Customer_Group_Metadata_20251022163324-20251022.xml
    Folder:
    TWO RIVERS
    List Price:
    $24.99
    As low as:
    $21.49
    Publisher Identifier:
    P-PER
    Discount Code:
    C
    Case Pack:
    60
    Country of Origin:
    United States
    Pub Discount:
    60
    Weight:
    5.6oz
    Imprint:
    O'Reilly Media
  • Overview

    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.

    Authors Ellen Friedman and Kostas Tzoumas show technical and nontechnical readers alike how Flink is engineered to overcome significant tradeoffs that have limited the effectiveness of other approaches to stream processing. You’ll also learn how Flink has the ability to handle both stream and batch data processing with one technology.

    • Learn the consequences of not doing streaming well—in retail and marketing, IoT, telecom, and banking and finance
    • Explore how to design data architecture to gain the best advantage from stream processing
    • Get an overview of Flink’s capabilities and features, along with examples of how companies use Flink, including in production
    • Take a technical dive into Flink, and learn how it handles time and stateful computation
    • Examine how Flink processes both streaming (unbounded) and batch (bounded) data without sacrificing performance