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
Hands-on Practical: Building a Custom Trigger
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- Apache Flink Documentation: Windowing, The Apache Flink Community, 2024 (Apache Software Foundation) - Official guide to Flink's windowing API, including how to implement custom triggers, manage timers, and handle TriggerResult actions.
- Stream Processing with Apache Flink: Fundamentals, StreamSQL, and Data-Driven Applications, Fabian Hueske and Vasia Kalavri, 2019 (O'Reilly Media) - A comprehensive resource by Flink contributors that details Flink's architecture, windowing mechanisms, and advanced features like custom trigger implementation and state management.
- Apache Flink Documentation: State & Fault Tolerance, The Apache Flink Community, 2024 (Apache Software Foundation) - Explains Flink's managed state, including ValueState and state cleanup practices, which are essential for building robust and fault-tolerant custom triggers.
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