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Stream Processing with Apache Spark (Mastering Structured Streaming and Spark Streaming)

List Price: $69.99
SKU:
9781491944240
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  • Product Details

    Author:
    Gerard Maas, Francois Garillot
    Format:
    Paperback
    Pages:
    450
    Publisher:
    O'Reilly Media (July 16, 2019)
    Language:
    English
    ISBN-13:
    9781491944240
    ISBN-10:
    1491944242
    Dimensions:
    7" x 9.19"
    File:
    TWO RIVERS-PERSEUS-Metadata_Only_Perseus_Distribution_Customer_Group_Metadata_20251023163248-20251023.xml
    Folder:
    TWO RIVERS
    List Price:
    $69.99
    As low as:
    $60.19
    Publisher Identifier:
    P-PER
    Discount Code:
    C
    Case Pack:
    9
    Country of Origin:
    United States
    Pub Discount:
    60
    Weight:
    25.12oz
    Imprint:
    O'Reilly Media
  • Overview

    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