Spark ─ The Definitive Guide; Big Data Processing Made Simple

Learn how to use, deploy, and maintain Apache Spark with this comprehensive guide, written by the creators of this 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 Spark’s Structured Streaming and MLlib for machine learning tasks
• Explore the wider Spark ecosystem, including SparkR and Graph Analysis
• Examine Spark deployment, including coverage of Spark in the Cloud

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.