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 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

• Hadoop: The Definitive Guide: Storage and Analysis at Internet Scale
• Learning Spark: Lightning-Fast Big Data Analysis
• Stream Processing with Apache Spark: Mastering Structured Streaming and Spark Streaming
• High Performance Spark: Best Practices for Scaling and Optimizing Apache Spark
• Advanced Analytics with Spark: Patterns for Learning from Data at Scale
• Programming in Scala: Updated for Scala 2.12
• Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems
• Kafka: The Definitive Guide: Real-Time Data and Stream Processing at Scale
• Database Internals: A Deep Dive into How Distributed Data Systems Work
• Streaming Systems: The What, Where, When, and How of Large-Scale Data Processing