Stream Processing with Apache Spark: Mastering Structured Streaming and Spark Streaming

by Gerard Maas, Francois Garillot

To build analytics tools that provide faster insights, knowing how to process data in real time is a must, and moving from batch processing to stream processing is absolutely required. Fortunately, the Spark in-memory framework/platform for processing data has added an extension devoted to fault-tolerant stream processing: Spark Streaming.

If you're familiar with Apache Spark and want to learn how to implement it for streaming jobs, this practical book is a must.


Understand how Spark Streaming fits in the big picture
Learn core concepts such as Spark RDDs, Spark Streaming clusters, and the fundamentals of a DStream
Discover how to create a robust deployment
Dive into streaming algorithmics
Learn how to tune, measure, and monitor Spark Streaming

Genres: Technology

452 pages, Paperback

Published June 17, 2019

Reviews

[Alex Ott · Rating 4 out of 5]

Very good description of Spark Streaming & Spark Structured Streaming, with many examples, and useful tips. Book is quite new, and covers latest developments (Spark 2.3, and mentions 2.4 in several places) in area of streaming data processing in Spark

I really wish I had this book ~4 years ago when we build our system on the top of the Spark Streaming. One star was taken because of the errors...

[Lorenzo Boccalon · Rating 4 out of 5]

A complete manual on Apache Spark streaming components. It is detailed and contains a few practical examples. I didn't check the online resources but they seem a good supplement. I skipped part 3 as it covers the "legacy" streaming component.

[Michael A. Bodkin]

Very helpful.