Hands-on guide to Apache Spark 3 : build scalable computing engines for batch and stream data processing / Alfonso Antoln̕ez Garca̕.

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Hands-on guide to Apache Spark 3 : build scalable computing engines for batch and stream data processing / Alfonso Antoln̕ez Garca̕.

9781484293805 (electronic bk.)
1484293800 (electronic bk.)
9781484293799
1484293797

New York : Apress, [2023]

©2023

English

1 online resource (xiii, 403 pages) : illustrations (chiefly color)

10.1007/978-1-4842-9380-5 doi

QA76.9.D343

006.3/12

This book explains how to scale Apache Spark 3 to handle massive amounts of data, either via batch or streaming processing. It covers how to use Sparks structured APIs to perform complex data transformations and analyses you can use to implement end-to-end analytics workflows. This book covers Spark 3's new features, theoretical foundations, and application architecture. The first section introduces the Apache Spark ecosystem as a unified engine for large scale data analytics, and shows you how to run and fine-tune your first application in Spark. The second section centers on batch processing suited to end-of-cycle processing, and data ingestion through files and databases. It explains Spark DataFrame API as well as structured and unstructured data with Apache Spark. The last section deals with scalable, high-throughput, fault-tolerant streaming processing workloads to process real-time data. Here you'll learn about Apache Spark Streamings execution model, the architecture of Spark Streaming, monitoring, reporting, and recovering Spark streaming. A full chapter is devoted to future directions for Spark Streaming. With real-world use cases, code snippets, and notebooks hosted on GitHub, this book will give you an understanding of large-scale data analysis concepts--and help you put them to use. Upon completing this book, you will have the knowledge and skills to seamlessly implement large-scale batch and streaming workloads to analyze real-time data streams with Apache Spark. You will: Master the concepts of Spark clusters and batch data processing Understand data ingestion, transformation, and data storage Gain insight into essential stream processing concepts and different streaming architectures Implement streaming jobs and applications with Spark Streaming.

Includes bibliographical references and index.

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Online resource; title from PDF title page (SpringerLink, viewed June 20, 2023).

Print version: 9781484293799

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