Kafka

Event streaming on OpenShift with Red Hat Streams for Apache Kafka: clients, connectors, change data capture and DC/DR replication.

01
Overview
Brokers, topics, partitions and offsets, and when to choose Kafka over a message queue or Redis.
Coming soon
02
Kafka on OpenShift
Install Streams for Apache Kafka and create a KRaft cluster, node pools and topics as custom resources.
Coming soon
03
Topic design
Partitions and keys for ordering and scale, replication for durability, and retention or compaction.
Coming soon
04
Producers and consumers
Spring Boot, Quarkus and .NET clients, consumer groups and offsets, and the HTTP Bridge for REST access.
Coming soon
05
Delivery guarantees
At-least-once vs exactly-once, idempotent consumers and dead-letter topics: never post a payment twice.
Coming soon
06
Schema registry
Avro, Protobuf and JSON Schema in the Red Hat build of Apicurio Registry, and evolving events safely.
Coming soon
07
Kafka Connect
Connectors between Kafka and databases, files or object storage, run as KafkaConnect resources.
Coming soon
08
CDC with Debezium
Stream row changes from PostgreSQL and Oracle with Debezium, and publish events with the outbox pattern.
Coming soon
09
Stream processing
Filter, join and aggregate events as they arrive with Kafka Streams and Apache Flink.
Coming soon
10
Kafka for AI
Keep RAG indexes fresh from change events, and score transactions for fraud as they happen.
Coming soon
11
Security
TLS listeners, client authentication, ACLs and quotas, managed as KafkaUser resources.
Coming soon
12
Monitoring
Broker metrics, consumer lag and alerts in Prometheus and Grafana, and the Streams for Apache Kafka console.
Coming soon
13
Scaling and upgrades
Add brokers, rebalance with Cruise Control, offload old data with tiered storage, and run rolling upgrades.
Coming soon
14
DC/DR with MirrorMaker 2
Replicate topics and consumer offsets to the DR site, and plan failover and failback.
Coming soon