To build analytics tools that provide faster insights, knowing how to process data in real time is a must, and moving from batch processing to stream processing is absolutely required. Fortunately, the Spark in-memory framework/platform for processing data has added an extension devoted to fault-tolerant stream processing: Spark Streaming. If you're familiar with Apache Spark and want to learn how to implement it for streaming jobs, this practical book is a must. Understand how Spark Streaming fits in the big picture Learn core concepts such as Spark RDDs, Spark Streaming clusters, and the fundamentals of a DStream Discover how to create a robust deployment Dive into streaming algorithmics Learn how to tune, measure, and monitor Spark Streaming