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There's growing interest in learning how to analyze streaming data in large-scale systems such as web traffic, financial transactions, machine logs, industrial sensors, and many others. But analyzing data streams at scale has been difficult to do well-until now. This practical book delivers a deep introduction to Apache Flink, a highly innovative open source stream processor with a surprising range of capabilities.

Authors Ellen Friedman and Kostas Tzoumas show technical and nontechnical readers alike how Flink is engineered to overcome significant tradeoffs that have limited the effectiveness of other approaches to stream processing. You'll also learn how Flink has the ability to handle both stream and batch data processing with one technology.

Learn the consequences of not doing streaming well-in retail and marketing, IoT, telecom, and banking and finance
Explore how to design data architecture to gain the best advantage from stream processing
Get an overview of Flink's capabilities and features, along with examples of how companies use Flink, including in production
Take a technical dive into Flink, and learn how it handles time and stateful computation
Examine how Flink processes both streaming (unbounded) and batch (bounded) data without sacrificing performance



Chapter 1Why Apache Flink?
Consequences of Not Doing Streaming Well
Goals for Processing Continuous Event Data
Evolution of Stream Processing Technologies
First Look at Apache Flink
Flink in Production
Where Flink Fits
Chapter 2Stream-First Architecture
Traditional Architecture versus Streaming Architecture
Message Transport and Message Processing
The Transport Layer: Ideal Capabilities
Streaming Data for a Microservices Architecture
Beyond Real-Time Applications
Geo-Distributed Replication of Streams
Chapter 3What Flink Does
Different Types of Correctness
Hierarchical Use Cases: Adopting Flink in Stages
Chapter 4Handling Time
Counting with Batch and Lambda Architectures
Counting with Streaming Architecture
Notions of Time
Windows
Time Travel
Watermarks
A Real-World Example: Kappa Architecture at Ericsson
Chapter 5Stateful Computation
Notions of Consistency
Flink Checkpoints: Guaranteeing Exactly Once
Savepoints: Versioning State
End-to-End Consistency and the Stream Processor as a Database
Flink Performance: the Yahoo! Streaming Benchmark
Conclusion
Chapter 6Batch Is a Special Case of Streaming
Batch Processing Technology
Case Study: Flink as a Batch Processor
Appendix Additional Resources
Going Further with Apache Flink
Selected O'Reilly Publications by Ted Dunning and Ellen Friedman