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
Lambda versus Kappa Architecture
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- Big Data: Principles and Best Practices of Scalable Realtime Data Systems, Nathan Marz and James Warren, 2015 (Manning Publications) - The definitive resource for understanding the Lambda Architecture, covering its motivations, layered components, and patterns for fault-tolerant data processing.
- Questioning the Lambda Architecture, Jay Kreps, 2014 Confluent Blog (Confluent) - Introduces the Kappa architecture as an alternative to Lambda, advocating for a log-centric approach where all data processing is unified as stream processing.
- Apache Flink Documentation, The Apache Flink Community, 2025 - Provides comprehensive information on Flink's capabilities, including its unified batch and stream processing model, exactly-once semantics, and state management, which enable the Kappa architecture.
- Apache Kafka Documentation, The Apache Kafka Community, 2024 - Details Kafka's role as a distributed commit log, its guarantees, and features that position it as the immutable source of truth for modern data architectures like Kappa.
- Stream Processing with Apache Flink: Fundamentals, StreamSQL, and Table API, Fabian Hueske and Vasia Kalavri, 2019 (O'Reilly Media) - A practical guide to building real-time data pipelines with Apache Flink, covering processing semantics, state management, and the architectural implications for stream-first designs.
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