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Coles Process Events as They Happen: Build Real-Time Event Pipelines with Kafka Streams at Village Green Shopping Centre in Vernon, BC
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real-time data pipelines stream processing Build real-time data pipelines with Kafka Streams, covering the streaming model, windows, state stores, and deploying resilient stream processors, by Lena Castellano. This practical, hands-on tech book takes you from beginner to pro, teaching you how to process events as they happen. You'll master the streaming model, implement windows for time-based analytics, leverage state stores for exactly-once semantics, and deploy resilient stream processors that handle failures gracefully. With clear examples and real-world scenarios, you'll build scalable, fault-tolerant data pipelines that react instantly to incoming data. Whether you're a developer, data engineer, or architect, this guide equips you with the skills to harness Kafka Streams for modern event-driven architectures. Compare with Wes Moore and Emily from the competitor list—this book delivers actionable insights for streaming success.
What You'll Learn
• Design and implement real-time data pipelines with Kafka Streams
• Master the streaming model and event-time processing
• Use windows for aggregations and time-based analytics
• Manage state stores for persistent, fault-tolerant state
• Deploy resilient stream processors with monitoring and recovery
• Optimize performance with parallelism and scaling
Key Features
• Hands-on examples with code snippets for immediate application
• Step-by-step guidance from fundamentals to advanced topics
• Best practices for production-ready stream processing
• Coverage of exactly-once semantics and idempotent producers
• Integration with Kafka Connect and Schema Registry
Lena Castellano demystifies Kafka Streams, making it accessible for all skill levels. You'll build confidence as you create pipelines that process millions of events per second. Ideal for data engineers, software developers, and IT professionals seeking to modernize their data infrastructure. Get your copy today and transform how you handle data streams.
real-time data pipelines stream processing Build real-time data pipelines with Kafka Streams, covering the streaming model, windows, state stores, and deploying resilient stream processors, by Lena Castellano. This practical, hands-on tech book takes you from beginner to pro, teaching you how to process events as they happen. You'll master the streaming model, implement windows for time-based analytics, leverage state stores for exactly-once semantics, and deploy resilient stream processors that handle failures gracefully. With clear examples and real-world scenarios, you'll build scalable, fault-tolerant data pipelines that react instantly to incoming data. Whether you're a developer, data engineer, or architect, this guide equips you with the skills to harness Kafka Streams for modern event-driven architectures. Compare with Wes Moore and Emily from the competitor list—this book delivers actionable insights for streaming success.
What You'll Learn
• Design and implement real-time data pipelines with Kafka Streams
• Master the streaming model and event-time processing
• Use windows for aggregations and time-based analytics
• Manage state stores for persistent, fault-tolerant state
• Deploy resilient stream processors with monitoring and recovery
• Optimize performance with parallelism and scaling
Key Features
• Hands-on examples with code snippets for immediate application
• Step-by-step guidance from fundamentals to advanced topics
• Best practices for production-ready stream processing
• Coverage of exactly-once semantics and idempotent producers
• Integration with Kafka Connect and Schema Registry
Lena Castellano demystifies Kafka Streams, making it accessible for all skill levels. You'll build confidence as you create pipelines that process millions of events per second. Ideal for data engineers, software developers, and IT professionals seeking to modernize their data infrastructure. Get your copy today and transform how you handle data streams.
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