82 event driven kafka
In this section
- Build Your Kafka SkillCreate your Kafka event schema skill in one prompt, then learn to improve it throughout the chapter
- JIT Chapter 82: Event-Driven Architecture with KafkaBuild your Kafka skill first, then learn to understand and refine it into a production-ready asset
- From Request-Response to EventsUnderstand why direct API calls between services create tight coupling and how asynchronous events solve temporal, availability, and behavioral coupling problems
- Event-Driven Architecture ConceptsMaster the fundamental concepts of event-driven architecture—events vs commands, eventual consistency, and when to use EDA over synchronous APIs.
- How Kafka Fits: The Mental ModelBuild a clear mental model of Kafka's architecture using visual analogies - topics, partitions, consumer groups, and the message journey
- Deploying Kafka with StrimziDeploy Apache Kafka on Kubernetes using the Strimzi operator with KRaft mode
- Your First Producer (Python)Build your first Kafka producer with confluent-kafka-python, learning delivery callbacks, poll(), and flush() patterns
- Producer Deep Dive: ReliabilityMaster Kafka producer reliability with acks configurations, idempotent producers, and delivery failure handling for production systems
- Your First Consumer (Python)Implement Kafka consumers with proper poll loops, offset management, and error handling using confluent-kafka-python
- Consumer Deep Dive: Groups and RebalancingMaster consumer group mechanics, implement rebalance callbacks, and diagnose consumer lag for reliable event processing
- Async Producers and Consumers in FastAPIIntegrate Kafka with FastAPI using async patterns, lifespan events, and background consumers for production event-driven APIs
- Message Schemas: Avro and Schema RegistryDesign event schemas with Apache Avro, integrate Confluent Schema Registry for centralized schema management, and implement backward-compatible schema evolution
- Delivery Semantics Deep DiveMaster at-most-once, at-least-once, and exactly-once delivery semantics in Kafka with practical decision frameworks and idempotent consumer patterns
- Transactions for Stream ProcessingImplement exactly-once stream processing with Kafka transactions, transactional producers, and the read-process-write pattern for atomic multi-topic operations
- Reliability ConfigurationConfigure min.insync.replicas and tune Kafka for latency versus durability trade-offs in production event-driven systems
- Kafka Connect: Building Data PipelinesDeploy Kafka Connect on Kubernetes using Strimzi, configure source and sink connectors, and build automated data pipelines without writing custom code
- Change Data Capture with DebeziumImplement the transactional outbox pattern using Debezium CDC to capture PostgreSQL changes and stream them to Kafka with guaranteed atomicity
- Agent Event PatternsDesign event schemas for AI agent communication with correlation tracking, implement agent-to-agent messaging patterns, and build notification fanout systems
- Saga Pattern for Multi-Step WorkflowsImplement choreography-based sagas using Kafka events and design compensating transactions for reliable distributed workflows
- Production Kafka with StrimziConfigure production-grade Kafka clusters with separate controller and broker node pools, TLS encryption, SCRAM authentication, and proper resource limits
- Monitoring and Debugging KafkaMaster consumer lag diagnosis, use Kafka CLI tools for troubleshooting, and configure alerting thresholds for production reliability
- AI-Assisted Kafka DevelopmentMaster effective AI collaboration for Kafka development: leverage AI's pattern knowledge, teach it your production constraints, and iterate toward optimal solutions
- Capstone: Event-Driven Agent NotificationsBuild a complete event-driven notification system using spec-driven development, integrating Task API events with notification and audit services
Capstone: Production AI Agent Chart
Specification-first development produces production-grade Helm charts by establishing clear success criteria and acceptance tests before implementation, then synthesizing all chapter concepts into a.
Build Your Kafka Skill
Create your Kafka event schema skill in one prompt, then learn to improve it throughout the chapter