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