Graph Algorithms: Practical Examples in Apache Spark and Neo4j | Stream Processing with Apache Flink: Fundamentals, Implementation, and Operation of Streaming Applications | Spark: The Definitive Guide: Big Data Processing Made Simple | The Rust Programming Language | Advanced Analytics with Spark: Patterns for Learning from Data at Scale | High Performance Spark | Kafka | Designing Data-intensive Applications | Functional Programming in Scala
Before you can build analytics tools to gain quick insights, you first need to know how to process data in real time. With this practical guide, developers familiar with Apache Spark will learn how to put this in-memory framework to use for streaming data. Youâll discover how Spark enables you to write streaming jobs in almost the same way you write batch jobs.
Authors Gerard Maas and FranÃ§ois Garillot help you explore the theoretical underpinnings of Apache Spark. This comprehensive guide features two sections that compare and contrast the streaming APIs Spark now supports: the original Spark Streaming library and the newer Structured Streaming API.
- Learn fundamental stream processing concepts and examine different streaming architectures
- Explore Structured Streaming through practical examples; learn different aspects of stream processing in detail
- Create and operate streaming jobs and applications with Spark Streaming; integrate Spark Streaming with other Spark APIs
- Learn advanced Spark Streaming techniques, including approximation algorithms and machine learning algorithms
- Compare Apache Spark to other stream processing projects, including Apache Storm, Apache Flink, and Apache Kafka Streams
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