Two days, from first message to production
Day 1
Fundamentals & Development
- Kafka architecture: Brokers, topics, partitions, replication, offsets — the foundation, properly understood
- Producers: Serializers, partitioners, delivery guarantees (acks=0/1/all), batch behavior
- Consumers: Consumer groups, offset management, rebalancing, manual commit
- Kafka CLI: Creating topics, producing and consuming messages, inspecting consumer group state
- Spring Kafka: @KafkaListener, KafkaTemplate, retry policy, Dead Letter Queue (DLQ)
- Schema Registry: Avro serialization, schema evolution, compatibility levels
Day 2
Streams, Security & Operations
- Kafka Streams: Topology concept, KStream vs KTable, aggregations, windowing, joins
- Exactly-Once Semantics (EOS): Idempotent producers, transactions — when they're actually necessary
- Topic configuration: Retention, log compaction, partitioning strategy for scale
- Kafka Connect: Source and sink connectors, offset management, error handling
- Monitoring: Tracking consumer lag, JMX metrics, building a Grafana dashboard for Kafka
- Kafka on Kubernetes: Strimzi Operator — deploying clusters, managing topics as custom resources
What your team walks away with
Kafka isn't a tool you fully grasp in an afternoon — but after two days, the core concepts should be solid enough that your team can move forward independently. Concretely:
- Design event-driven systems correctly — knowing when Kafka is the right choice and when it isn't
- Implement producers and consumers in Java/Spring Boot, choosing delivery guarantees deliberately
- Use Kafka Streams for stream processing logic without pulling in external frameworks
- Size topic configuration and partitioning for production workloads
- Monitor consumer lag and catch problems early — before they reach end users
All exercises run on a real Kafka cluster. Participants see how configuration changes affect behavior under load — not just on a diagram.
Book the Kafka training
Whether it's a fundamentals introduction or a targeted deep-dive into Kafka Streams and production operations — short call, then a concrete proposal.
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