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
The ProcessFunction Hierarchy
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- ProcessFunction, Apache Flink Community, 2025 (Apache Flink) - Provides an in-depth guide to using ProcessFunction and its specialized variants, detailing how to leverage state, timers, and side outputs for fine-grained control over stream processing logic.
- State & Fault Tolerance, Apache Flink Community, 2024 - Explains the core principles of stateful stream processing in Flink, covering managed keyed state, operator state, broadcast state, and the mechanisms that ensure fault tolerance and consistency, which are foundational for ProcessFunction implementations.
- Stream Processing with Apache Flink: Fundamentals, Streamlined Data Pipelines, and Advanced Production Deployments, Fabian Hueske, Vasiliki Kalavri, 2019 (O'Reilly Media) - A comprehensive guide to building stream processing applications with Flink, offering detailed coverage of low-level APIs like ProcessFunction for complex event processing, state management, and advanced time-based operations.
- Event Time, Apache Flink Community, 2024 (Apache Flink Community) - Clarifies Flink's different time characteristics, with a particular focus on event time and watermarks, which are essential for understanding how ProcessFunctions handle timers and ensure correct processing order in event-driven systems.
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