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
Failure Recovery Strategies
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- Fault Tolerance, The Apache Flink Community, 2025 (Apache Flink Project) - Official documentation for Flink's fault tolerance mechanisms, including restart strategies, checkpointing, and high availability configuration.
- Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems, Martin Kleppmann, 2017 (O'Reilly Media) - A comprehensive book on distributed systems, offering foundational knowledge on reliability, consistency, and various data processing patterns relevant to robust pipelines.
- Apache Flink: Stream Processing at Scale, Alexander Alexandrov, Rico Bergmann, Paris Carbone, Maximilian E. Schwalbe, Marcin Staniewski, Kostas Tzoumas, Stephan Ewen, Robert Hirschfeld, Johannes Kirschnick, Johann-Christoph Freytag, Felix Naumann, 2017 Proceedings of the VLDB Endowment, Vol. 10 (VLDB Endowment) DOI: 10.14778/3137628.3137629 - Foundational paper detailing the design and implementation of Apache Flink, focusing on its distributed architecture and fault tolerance model through lightweight asynchronous snapshots.
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