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
Kafka Consumer Lag Analysis
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- Apache Kafka Documentation - Consumers, The Apache Software Foundation, 2024 (The Apache Software Foundation) - Official documentation covering Kafka consumer fundamentals, including offsets, consumer groups, and their role in understanding lag.
- Kafka: The Definitive Guide: Real-Time Data and Stream Processing at Scale, Neha Narkhede, Gwen Shapira, Todd Palino, 2017 (O'Reilly Media) - A comprehensive guide to Apache Kafka, offering detailed explanations of consumer groups, partitions, offset management, and strategies for monitoring and tuning performance related to consumer lag.
- Stream Processing with Apache Flink: Fundamentals, StreamSQL, and Table API, Fabian Hueske, Vasia Kalavri, 2019 (O'Reilly Media) - An authoritative book on Apache Flink, explaining its architecture, state management, checkpointing mechanism, and how these impact real-time data processing and lag analysis in Flink applications.
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