Spark in Action

Summary

Spark in Action teaches you the theory and skills you need to effectively handle batch and streaming data using Spark. Fully updated for Spark 2.0.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the Technology

Big data systems distribute datasets across clusters of machines, making it a challenge to efficiently query, stream, and interpret them. Spark can help. It is a processing system designed specifically for distributed data. It provides easy-to-use interfaces, along with the performance you need for production-quality analytics and machine learning. Spark 2 also adds improved programming APIs, better performance, and countless other upgrades.

About the Book

Spark in Action teaches you the theory and skills you need to effectively handle batch and streaming data using Spark. You’ll get comfortable with the Spark CLI as you work through a few introductory examples. Then, you’ll start programming Spark using its core APIs. Along the way, you’ll work with structured data using Spark SQL, process near-real-time streaming data, apply machine learning algorithms, and munge graph data using Spark GraphX. For a zero-effort startup, you can download the preconfigured virtual machine ready for you to try the book’s code.

What’s Inside

• Updated for Spark 2.0
• Real-life case studies
• Spark DevOps with Docker
• Examples in Scala, and online in Java and Python

About the Reader

Written for experienced programmers with some background in big data or machine learning.

About the Authors

Petar Zečević and Marko Bonaći are seasoned developers heavily involved in the Spark community.

Table of Contents

Part 1: First steps
Chapter 1: Introduction to Apache Spark
Chapter 2: Spark fundamentals
Chapter 3: Writing Spark applications
Chapter 4: The Spark API in depth

Part 2: Meet the Spark family
Chapter 5: Sparkling queries with Spark SQL
Chapter 6: Ingesting data with Spark Streaming
Chapter 7: Getting smart with MLlib
Chapter 8: ML: classification and clustering
Chapter 9: Connecting the dots with GraphX

Part 3: Spark ops
Chapter 10: Running Spark
Chapter 11: Running on a Spark standalone cluster
Chapter 12: Running on YARN and Mesos

Part 4: Bringing it together
Chapter 13: Case study: real-time dashboard
Chapter 14: Deep learning on Spark with H2O

Appendix A: Installing Apache Spark
Appendix B: Understanding MapReduce
Appendix C: A primer on linear algebra