Spark: The Definitive Guide: Big Data Processing Made Simple

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"O'Reilly Media, Inc.", Feb 8, 2018 - Computers - 606 pages

Learn how to use, deploy, and maintain Apache Spark with this comprehensive guide, written by the creators of the open-source cluster-computing framework. With an emphasis on improvements and new features in Spark 2.0, authors Bill Chambers and Matei Zaharia break down Spark topics into distinct sections, each with unique goals.

You’ll explore the basic operations and common functions of Spark’s structured APIs, as well as Structured Streaming, a new high-level API for building end-to-end streaming applications. Developers and system administrators will learn the fundamentals of monitoring, tuning, and debugging Spark, and explore machine learning techniques and scenarios for employing MLlib, Spark’s scalable machine-learning library.

  • Get a gentle overview of big data and Spark
  • Learn about DataFrames, SQL, and Datasets—Spark’s core APIs—through worked examples
  • Dive into Spark’s low-level APIs, RDDs, and execution of SQL and DataFrames
  • Understand how Spark runs on a cluster
  • Debug, monitor, and tune Spark clusters and applications
  • Learn the power of Structured Streaming, Spark’s stream-processing engine
  • Learn how you can apply MLlib to a variety of problems, including classification or recommendation
 

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Preview looks awesome. Its complete package of Spark concepts as Hadoop definitive guide is for Hadoop.

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Contents

Section 1
Section 2
Section 3
Section 4
Section 5
Section 6
Section 7
Section 8
Section 21
Section 22
Section 23
Section 24
Section 25
Section 26
Section 27
Section 28

Section 9
Section 10
Section 11
Section 12
Section 13
Section 14
Section 15
Section 16
Section 17
Section 18
Section 19
Section 20
Section 29
Section 30
Section 31
Section 32
Section 33
Section 34
Section 35
Section 36
Section 37
Section 38
Section 39
Copyright

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About the author (2018)

Bill Chambers is a Product Manager at Databricks focusing on large-scale analytics, strong documentation, and collaboration across the organization to help customers succeed with Spark and Databricks. He has a Master's degree in Information Systems from the UC Berkeley School of Information, where he focused on data science.

Matei Zaharia is an assistant professor of computer science at Stanford University and Chief Technologist at Databricks. He started the Spark project at UC Berkeley in 2009, where he was a PhD student, and he continues to serve as its vice president at Apache. Matei also co-started the Apache Mesos project and is a committer on Apache Hadoop. Matei’s research work was recognized through the 2014 ACM Doctoral Dissertation Award and the VMware Systems Research Award.

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