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
Model Serving Patterns in Streams
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- Asynchronous I/O for External Services, The Apache Flink Community, 2024 (Apache Software Foundation) - Explains the Flink API fundamental for implementing the external model serving pattern with low-latency communication to external services.
- Stream Processing with Apache Flink: Fundamentals, StreamSQL, and Real-time Analytics, Fabian Hueske, Vasiliki Kalavri, 2019 (O'Reilly Media) - A foundational book covering the principles and implementation of stream processing with Apache Flink, providing context for building real-time pipelines.
- Designing Machine Learning Systems: An Iterative Process for Production-Ready ML Applications, Chip Huyen, 2022 (O'Reilly Media) - Discusses various machine learning system design patterns, including strategies for model deployment and real-time serving in production environments.
- NVIDIA Triton Inference Server Documentation, NVIDIA, 2024 - Provides detailed information on a high-performance, open-source inference serving solution, exemplifying the external service pattern for ML models.
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