Constructing resilient, low-latency data pipelines requires mastering the interoperability between message brokers and stream processing engines. This course examines the advanced architectural patterns and implementation details necessary to build production-grade streaming systems using Apache Kafka and Apache Flink. You will analyze distributed system consistency, state management strategies, and performance tuning techniques specific to high-throughput environments.
The curriculum progresses through internal mechanisms of Kafka's transactional protocols and Flink's checkpointing algorithms. You will implement exactly-once processing semantics, manage large state with RocksDB, and handle complex event processing scenarios. The content addresses specific challenges in real-time AI, such as online feature engineering and model serving over streams. By the end, you will possess the technical capability to architect, deploy, and optimize data streams that serve machine learning models and analytics dashboards with millisecond latency.
Prerequisites Kafka, Flink, and Java/Scala
Level:
Architecture
Design fault-tolerant Kappa architectures that unify batch and stream processing.
Reliability
Implement exactly-once processing semantics using Kafka transactions and Flink two-phase commits.
State Management
Configure and tune RocksDB state backends for massive state management in streaming applications.
Optimization
Diagnose backpressure, optimize serialization, and tune parallelism for high-throughput environments.