Day 11 of 14 35 minStatelessStatefulWindows
Process events continuously as they arrive. Build stateless steps (filter, transform, route) and stateful ones (counts and totals per key), group events into time windows, enrich a stream by joining it with a table, and meet the tools that do this at scale.
By the end of today you can
- Build stateless stream steps that filter, transform and route events
- Keep state per key for running counts and totals
- Group events into tumbling windows by event time
- Enrich a stream by joining it with a table built from a compacted topic
- Choose between Kafka Streams, ksqlDB, Flink and Python libraries
What's inside
- 5 sections
- 4 diagrams and mind maps
- 4 code examples you can run
- 5-question quiz
- 1Stateless steps: filter, transform, route
- 2Stateful steps: counts and totals per key
- 3Windows: aggregating over time
- 4Joins: enriching a stream
- 5Tools for stream processing
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