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Mastering Kafka Streams and ksqlDB (Building Real-Time Data Systems by Example)

List Price: $79.99
SKU:
9781492062493
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  • Product Details

    Author:
    Mitch Seymour
    Format:
    Paperback
    Pages:
    432
    Publisher:
    O'Reilly Media (March 16, 2021)
    Language:
    English
    ISBN-13:
    9781492062493
    ISBN-10:
    1492062499
    Dimensions:
    7" x 9.19"
    File:
    TWO RIVERS-PERSEUS-Metadata_Only_Perseus_Distribution_Customer_Group_Metadata_20251023163248-20251023.xml
    Folder:
    TWO RIVERS
    List Price:
    $79.99
    As low as:
    $68.79
    Publisher Identifier:
    P-PER
    Discount Code:
    C
    Case Pack:
    9
    Country of Origin:
    United States
    Pub Discount:
    60
    Weight:
    24.16oz
    Imprint:
    O'Reilly Media
  • Overview

    Working with unbounded and fast-moving data streams has historically been difficult. But with Kafka Streams and ksqlDB, building stream processing applications is easy and fun. This practical guide shows data engineers how to use these tools to build highly scalable stream processing applications for moving, enriching, and transforming large amounts of data in real time.

    Mitch Seymour, data services engineer at Mailchimp, explains important stream processing concepts against a backdrop of several interesting business problems. You'll learn the strengths of both Kafka Streams and ksqlDB to help you choose the best tool for each unique stream processing project. Non-Java developers will find the ksqlDB path to be an especially gentle introduction to stream processing.

    • Learn the basics of Kafka and the pub/sub communication pattern
    • Build stateless and stateful stream processing applications using Kafka Streams and ksqlDB
    • Perform advanced stateful operations, including windowed joins and aggregations
    • Understand how stateful processing works under the hood
    • Learn about ksqlDB's data integration features, powered by Kafka Connect
    • Work with different types of collections in ksqlDB and perform push and pull queries
    • Deploy your Kafka Streams and ksqlDB applications to production