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
Hands-on Practical: Real-Time Inference Pipeline
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- Apache Flink Documentation, Apache Flink Community, 2025 (Apache Software Foundation) - Official guide for Apache Flink, covering its architecture, stateful stream processing, custom operators, and deployment best practices for building real-time pipelines.
- Stream Processing with Apache Flink: Fundamentals, StreamSQL, and Data-Driven Applications, Fabian Hueske and Vasia Kalavri, 2019 (O'Reilly Media) - Provides an in-depth understanding of Flink's stream processing capabilities, including state management, event-time processing, and the development of custom functions for real-time applications.
- Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications, Chip Huyen, 2022 (O'Reilly Media) - Addresses architectural considerations for building robust machine learning systems, including strategies for online inference, real-time feature engineering, and optimizing for low-latency predictions.
- Predictive Model Markup Language (PMML) Specification (Version 4.4.1), Data Mining Group, 2019 (Data Mining Group) - The official standard for an XML-based language to represent predictive models, facilitating model exchange and deployment across diverse platforms for embedded inference.
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