Overview

APACHE FLINK IN ACTION: Stream Processing, Event-Driven Systems, and Real-Time Analytics at Scale What if your systems could react instantly to every click, payment, sensor reading, or anomaly, no delays, no stale data, no compromises? And what if you could build those real-time capabilities with confidence, correctness, and performance guaranteed? Apache Flink makes that possible, and this book shows you exactly how. At its core, Apache Flink in Action tackles one of the defining challenges of modern data engineering: designing and operating real-time, stateful stream processing systems that are fast, reliable, and scalable. Whether you're building fraud detectors, personalisation engines, analytics platforms, or event-driven microservices, this book equips you to use Flink as the backbone of your architecture. You'll learn how to reason about time, state, and consistency, the pillars of accurate streaming analytics, and how to apply them across real production workloads. You'll see how to combine streaming SQL with the DataStream API, how to engineer high-throughput pipelines, and how to deploy resilient Flink clusters on Kubernetes. You'll also gain the operational instincts needed to handle backpressure, schema evolution, checkpoint failures, and real-world incidents. Expect to walk away with the ability to design and operate real systems at scale, supported by practical patterns taken directly from chapters such as: - State Management & Checkpointing Essentials - Build fault-tolerant pipelines with exactly-once guarantees. - Writing Resilient Streaming Logic - Handle late data, watermark strategies, complex event patterns, and streaming joins. - Performance Tuning & Resource Engineering - Optimize CPU, GC, memory, and checkpoint overhead for high-throughput workloads. - Deployment & Infrastructure Recipes - Run Flink in standalone, YARN, Kubernetes, and fully native modes with HA enabled. - Observability & Incident Response - Monitor metrics, trace end-to-end flows, and apply structured debugging techniques. - Machine Learning & Feature Engineering in Real Time - Score models in-stream and maintain evolving features. - Migration & Comparative Patterns - Move from Spark Streaming, Kafka Streams, or Samza with clarity and confidence. - Production Case Studies - Learn from real architectures in clickstream analytics, fraud detection, and sessionization. If you want to build systems that think and react in real time, and you want to do it with the leading stream processing framework, then this is the book you need. Get your copy now and start building real-time data systems that truly scale.

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