Spark in Action, Second Edition

  • 8h 10m
  • Jean-Georges Perrin
  • Manning Publications
  • 2020

The Spark distributed data processing platform provides an easy-to-implement tool for ingesting, streaming, and processing data from any source. In Spark in Action, Second Edition, you’ll learn to take advantage of Spark’s core features and incredible processing speed, with applications including real-time computation, delayed evaluation, and machine learning. Spark skills are a hot commodity in enterprises worldwide, and with Spark’s powerful and flexible Java APIs, you can reap all the benefits without first learning Scala or Hadoop.

About the technology

Analyzing enterprise data starts by reading, filtering, and merging files and streams from many sources. The Spark data processing engine handles this varied volume like a champ, delivering speeds 100 times faster than Hadoop systems. Thanks to SQL support, an intuitive interface, and a straightforward multilanguage API, you can use Spark without learning a complex new ecosystem.

About the book

Spark in Action, Second Edition, teaches you to create end-to-end analytics applications. In this entirely new book, you’ll learn from interesting Java-based examples, including a complete data pipeline for processing NASA satellite data. And you’ll discover Java, Python, and Scala code samples hosted on GitHub that you can explore and adapt, plus appendixes that give you a cheat sheet for installing tools and understanding Spark-specific terms.

What's inside

  • Writing Spark applications in Java
  • Spark application architecture
  • Ingestion through files, databases, streaming, and Elasticsearch
  • Querying distributed datasets with Spark SQL

About the reader

This book does not assume previous experience with Spark, Scala, or Hadoop.

About the Author

Jean-Georges Perrin is an experienced data and software architect. He is France’s first IBM Champion and has been honored for 12 consecutive years.

In this Book

  • Foreword
  • About This Book
  • About the Cover Illustration
  • So, What is Spark, Anyway?
  • Architecture and Flow
  • The Majestic Role of the Dataframe
  • Fundamentally Lazy
  • Building a Simple App for Deployment
  • Deploying Your Simple App
  • Ingestion from Files
  • Ingestion from Databases
  • Advanced Ingestion—Finding Data Sources and Building Your Own
  • Ingestion Through Structured Streaming
  • Working with SQL
  • Transforming Your Data
  • Transforming Entire Documents
  • Extending Transformations with User-Defined Functions
  • Aggregating Your Data
  • Cache and Checkpoint—Enhancing Spark’s Performances
  • Exporting Data and Building Full Data Pipelines
  • Exploring Deployment Constraints—Understanding the Ecosystem


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