Real-Time Data Pipelines with Apache Kafka and Flink
Chapter 1: Stream Processing Architectures and Semantics
Evolution of Distributed Logs
Lambda versus Kappa Architecture
Processing Guarantees and Semantics
Event Time versus Processing Time
Hands-on Practical: Designing a Kappa Pipeline
Chapter 2: Advanced Kafka Producer and Consumer Internals
Replication Protocols and ISRs
Idempotent Producers and Transactions
Custom Partitioning Strategies
Consumer Group Rebalancing protocols
Hands-on Practical: Implementing Transactional Writes
Chapter 3: Flink State Management and Checkpointing
State Backends: HashMap versus RocksDB
Asynchronous Barrier Snapshots
Incremental Checkpointing
State Schema Evolution
Hands-on Practical: State Migration with Savepoints
Chapter 4: Advanced Windowing and Time Attributes
Watermark Generation Strategies
Handling Late Data and Side Outputs
Custom Window Triggers and Evictors
Session Windows and Gap Analysis
Hands-on Practical: Building a Custom Trigger
Chapter 5: Low-Level Operations with ProcessFunctions
The ProcessFunction Hierarchy
Timer Services and Event Scheduling
Broadcast State Pattern
Async I/O for External Lookups
Hands-on Practical: Dynamic Rule Evaluation
Chapter 6: Production Deployment and Reliability
Serialization with Avro and Protobuf
Schema Registry Integration
Kafka Connect for Sink and Source
Failure Recovery Strategies
Hands-on Practical: Schema Evolution in Flight
Chapter 7: Performance Tuning and Monitoring
Identifying Backpressure
Memory Management and Slot Allocation
Tuning RocksDB Performance
Kafka Consumer Lag Analysis
Hands-on Practical: Resolving Skewed Data
Chapter 8: Real-Time AI and Feature Engineering
Online Feature Generation
Model Serving Patterns in Streams
Request-Response over Async Streams
Feature Store Integration
Hands-on Practical: Real-Time Inference Pipeline
Watermark Generation Strategies
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- Working with Event Time, The Apache Flink Community, 2018 (Apache Software Foundation) - Official documentation for Apache Flink's event time processing, watermarks, watermark strategies, and idleness handling.
- Streaming Systems: The What, Where, When, and How of Large-Scale Data Processing, Tyler Akidau, Slava Chernyak, Reuven Lax, 2018 (O'Reilly Media) - A comprehensive guide covering the fundamental concepts of distributed stream processing, including event time, watermarks, windowing, and correctness guarantees.
- The Dataflow Model: A Practical Approach to Balancing Correctness, Latency, and Cost in Unified Stream Processing, Tyler Akidau, Robert Bradshaw, Craig Chambers, Slava Chernyak, Rafael J. Fernández-Moctezuma, Rui Li, Davorin Marković, Francis McCabe, Jean-Baptiste Onofré, Abigail P. O'Neil, Paul Nordstrom, and Sam Whittle, 2015 Proceedings of the VLDB Endowment, Vol. 8 (VLDB Endowment) DOI: 10.14778/2824032.2824076 - Foundational paper introducing the Dataflow Model, which influenced modern stream processing systems like Flink, detailing event time, processing time, and watermark semantics.
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