Hands-on Guide to Apache Spark 3: Build Scalable Computing Engines for Batch and Stream Data Processing - Softcover

Antol�nez Garc�a, Alfonso

9781484293812: Hands-on Guide to Apache Spark 3: Build Scalable Computing Engines for Batch and Stream Data Processing

Softcover

ISBN 10: 1484293819 ISBN 13: 9781484293812

Verlag: Apress, 2023

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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 Spark's 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 Streaming's 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.

What You Will Learn

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

Who This Book Is For

Data engineers, data analysts, machine learning engineers, Python and R programmers

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