Big data systems distribute datasets across clusters of machines, making it a challenge to efficiently query, stream, and interpret them. Spark can help. It is a processing system designed specifically for distributed data. It provides easy-to-use interfaces, along with the performance you need for production-quality analytics and machine learning. Spark 2 also adds improved programming APIs, better performance, and countless other upgrades.
Spark in Action teaches you the theory and skills you need to effectively handle batch and streaming data using Spark. You'll get comfortable with the Spark CLI as you work through a few introductory examples. Then, you'll start programming Spark using its core APIs. Along the way, you'll work with structured data using Spark SQL, process near-real-time streaming data, apply machine learning algorithms, and munge graph data using Spark GraphX. For a zero-effort startup, you can download the preconfigured virtual machine ready for you to try the book's code.
What's Inside
• Updated for Spark 2.0 • Real-life case studies • Spark DevOps with Docker • Examples in Scala, and online in Java and Python
Table of Contents PART 1 -FIRST STEPS • Introduction to Apache Spark • Spark fundamentals • Writing Spark applications • The Spark API in depth PART 2 -MEET THE SPARK FAMILY • Sparkling queries with Spark SQL • Ingesting data with Spark Streaming • Getting smart with MLlib • ML: classification and clustering • Connecting the dots with GraphX PART 3 -SPARK OPS • Running Spark • Running on a Spark standalone cluster • Running on YARN and Mesos PART 4 -BRINGING IT TOGETHER • Case study: real-time dashboard • Deep learning on Spark with H2O
