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: State Migration with Savepoints
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- Savepoints, Apache Flink Community, 2024 - Describes the purpose, mechanics, and usage of Savepoints for application upgrades and migrations within Apache Flink.
- The State Processor API, Apache Flink Community, 2019 (Apache Software Foundation) - Explains how to read, modify, and write Flink state from Savepoints or Checkpoints using batch processing, particularly useful for schema evolution.
- Streaming Systems: The What, Where, When, and How of Large-Scale Data Processing, Tyler Akidau, Slava Chernyak, Reuven Lax, 2018 (O'Reilly Media) - Provides fundamental principles of stateful stream processing, consistency models, and fault tolerance in large-scale streaming systems.
- Apache Flink: Stream Processing at Scale, Paris Carbone, Asterios Katsifodimos, Stephan Ewen, Volkan Özcelik, Kostas Tzoumas, and Daniel Warneke, 2015 Proceedings of the VLDB Endowment, Vol. 8 (The VLDB Endowment) DOI: 10.14778/2824032.2824047 - Describes the architecture and core components of Apache Flink, including its state management and fault tolerance mechanisms.
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