Event Streaming
Event streaming is a way to record and distribute facts as they happen, so many systems can react now or read the same history later.
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Intro
Event Streaming
Event streaming treats a business fact as data you can publish, retain, and process. An order placed, a payment authorized, and a device reading are events. They say that something happened at a point in time.
This changes how systems connect. A producer publishes an event to a named stream. Consumers read it independently. The producer does not need to know which consumers exist, and a new consumer can often read retained history.
A stream is not a database table, an RPC call, or a queue by default. It is an ordered record of events within a partition or shard. Ordering is normally scoped to that unit, not to an entire distributed system.
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Sources
- https://kafka.apache.org/intro/
Supports
- Events, producers, consumers, topics, partitions, brokers, and retained event history
- Per-partition ordering, key-based partition placement, consumer groups, and independent consumers
- Event streaming as publish, store, and process
- https://kafka.apache.org/documentation/#semantics
Supports
- At-most-once, at-least-once, and exactly-once delivery semantics
- Idempotent producers and transactional processing boundaries
- https://kafka.apache.org/documentation/#design
Supports
- Persistence, replication, and distributed design considerations
- https://cloudevents.io/
Supports
- CloudEvents as a specification for describing event data in a common way
- Event-data portability across services and platforms
- https://docs.nats.io/nats-concepts/what-is-nats
Supports
- NATS as a messaging system with publish and subscribe concepts
- https://github.com/manuzhang/awesome-streaming
Supports
- Curated ecosystem discovery for streaming frameworks and applications
- https://github.com/samber/awesome-olap
Supports
- Curated entries for Apache Pulsar, NATS JetStream, Apache Flink, and related streaming tools
- https://debezium.io/
Supports
- Debezium as an open-source change data capture platform that emits change events
- https://flink.apache.org/
Supports
- Apache Flink as a framework for stateful stream processing
- https://pulsar.apache.org/
Supports
- Apache Pulsar as a distributed messaging and streaming platform
- https://www.rabbitmq.com/streams.html
Supports
- RabbitMQ Streams as persistent append-only log streams