Streaming Systems: The What, Where, When, and How of Large-Scale Data Processing | Spark: The Definitive Guide: Big Data Processing Made Simple | Advanced Analytics with Spark: Patterns for Learning from Data at Scale | Learning TensorFlow: A Guide to Building Deep Learning Systems | Apache Spark in 24 Hours | High Performance Spark | Kafka | Statistics for Data Scientists | Learning Spark
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
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