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
Serialization with Avro and Protobuf
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- Apache Avro™ 1.11.1 Documentation, The Apache Software Foundation, 2024 (The Apache Software Foundation) - Official documentation for Apache Avro, providing specifications for its data serialization format and RPC protocol.
- Protocol Buffers, Google, 2024 - Official documentation for Google's Protocol Buffers, detailing its language-neutral, platform-neutral, extensible mechanism for serializing structured data.
- Confluent Platform Documentation - Schema Registry, Confluent, 2024 - Comprehensive guide to Confluent Schema Registry, covering its role in managing Avro schemas for Kafka-based data pipelines.
- Apache Flink Documentation - Type Information and Serialization, The Apache Software Foundation, 2024 - Official Flink documentation on how data types are handled and serialized, including the use of specific serializers and avoiding Kryo fallback.
- Kafka: The Definitive Guide: Real-time Data and Stream Processing at Scale, Gwen Shapira, Neha Narkhede, and Todd Palino, 2017 (O'Reilly Media) - Covers data serialization strategies for Kafka, including the benefits of binary formats and the role of Schema Registry.
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