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
Processing Guarantees and Semantics
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- Distributed snapshots: Determining global states of distributed systems, K. Mani Chandy and Leslie Lamport, 1985 ACM Transactions on Computer Systems, Vol. 3 (Association for Computing Machinery) DOI: 10.1145/214451.214456 - This foundational paper introduces the Chandy-Lamport algorithm for distributed snapshots, a core mechanism behind Flink's exactly-once semantics.
- State & Fault Tolerance, The Apache Flink Community, 2024 (Apache Software Foundation) - Official documentation explaining Flink's state management, checkpointing, and recovery mechanisms which enable exactly-once processing.
- Event Time, The Apache Flink Community, 2024 - Official documentation detailing event time, watermarks, and their role in consistent, deterministic stream processing results.
- Transactions, The Apache Kafka Community, 2024 - Official documentation describing Kafka's transactional API, indispensable for achieving end-to-end exactly-once semantics when integrating with stream processors like Flink.
- Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems, Martin Kleppmann, 2017 (O'Reilly Media) - A comprehensive book providing an in-depth treatment of consistency models, distributed transactions, and stream processing reliability, including idempotency and exactly-once semantics.
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