Spark in Action (《火花行動》)

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

PART 2 - MEET THE SPARK FAMILY

PART 3 - SPARK OPS

PART 4 - BRINGING IT TOGETHER

1. Introduction to Apache Spark
2. Spark fundamentals
3. Writing Spark applications
4. The Spark API in depth
5. Sparkling queries with Spark SQL
6. Ingesting data with Spark Streaming
7. Getting smart with MLlib
8. ML: classification and clustering
9. Connecting the dots with GraphX
10. Running Spark
11. Running on a Spark standalone cluster
12. Running on YARN and Mesos
13. Case study: real-time dashboard
14. Deep learning on Spark with H2O