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
Consumer Group Rebalancing protocols
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- Apache Kafka Documentation - Consumer Configs, Apache Kafka, 2024 - Provides official documentation for Apache Kafka consumer configurations, including parameters like max.poll.interval.ms, session.timeout.ms, heartbeat.interval.ms, group.instance.id, and partition.assignment.strategy, which are central to rebalancing behavior.
- KIP-345: Incremental Cooperative Rebalance Protocol, Jason Gustafson, Matthias J. Sax, 2018 Apache Kafka Improvement Proposal (KIP) (Apache Software Foundation) - The original design proposal introducing and detailing the Incremental Cooperative Rebalance Protocol, outlining its motivation, design principles, and operational mechanics.
- Introducing Apache Kafka’s Incremental Cooperative Rebalance, Jason Gustafson, 2019 (Confluent) - Offers a clear explanation of the Cooperative Rebalancing protocol, supplementing the KIP with practical context and advantages for users.
- Kafka: The Definitive Guide, Gwen Shapira, Todd Palino, Rajini Sivaram, Krit Petty, 2021 (O'Reilly Media) - Provides an extensive overview of Apache Kafka, including explanations of consumer groups, rebalancing protocols (both eager and cooperative), and various partition assignment strategies.
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