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
Custom Window Triggers and Evictors
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- Apache Flink Documentation: Windows, The Apache Flink Community, 2024 - Provides the official and most up-to-date information on Flink's windowing mechanisms, including detailed explanations of the Trigger and Evictor interfaces, their callbacks, and state management.
- Stream Processing with Apache Flink, Fabian Hueske, Vasiliki Kalavri, 2019 (O'Reilly Media) - Offers comprehensive coverage of Flink's stream processing capabilities, with dedicated sections on advanced windowing, custom triggers, and best practices for stateful stream processing.
- The Dataflow Model: A Practical Approach to Balancing Correctness, Latency, and Cost in Unified Stream and Batch Data Processing, Tyler Akidau, Alex Balikov, Kaya Bekiroğlu, Slava Chernyak, Jose Franco, Camuel Guérout, Reuven Lax, Sam McVeety, Daniel Mills, Frances Perry, Eric Schmidt, and Sam Whittle, 2017 Proceedings of the VLDB Endowment, Vol. 10 (VLDB Endowment) DOI: 10.14778/3137628.3137637 - Describes the fundamental concepts of the Dataflow Model, including event-time windows and speculative processing, which custom triggers in Flink build upon for advanced control.
- Apache Flink: Stream and Batch Processing in a Single Engine, Alexander Alexandrov, Rico Bergmann, Carsten Binnig, Mark Fischer, Pavel Gololobov, Fabian Hueske, Kostas Tzoumas, Albert Vilm, and Daqiang Zhang, 2015 Proceedings of the VLDB Endowment, Vol. 8 (VLDB Endowment) DOI: 10.14778/2824032.2824076 - Presents the foundational architecture of Apache Flink, explaining its unified stream and batch processing model, state management, and event-time capabilities that are fundamental to advanced windowing constructs like triggers and evictors.
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