Kafka clickstream streaming analytics pipeline
This data pipeline flowchart shows a streaming route for clickstream analytics. Web and mobile clients publish events to a Kafka topic, and a schema check immediately separates incomplete records from valid ones. Invalid events are stored in a dead-letter topic and the producing team is notified instead of allowing malformed payloads into downstream metrics.
Open it in the AI editor with a prompt pre-filled — keep what works, change what doesn't.
Scenario
A consumer product team needs near-real-time session and conversion metrics from web and mobile activity. Producers get notified when malformed events are retained in the dead-letter topic.
Key decisions
- Schema gate: Required event fields are checked before stream processing.
- Dead-letter retention: Bad events remain available for producer diagnosis.
- Privacy filter: Direct identifiers are removed before analytical storage.
- Windowed aggregation: Flink turns individual events into session and conversion metrics.
When to reuse this
Use this for a governed clickstream pipeline that serves product analytics while preserving bad-event evidence. It is appropriate when a Kafka topic has many independent producers.
Frequently asked questions
Why keep a dead-letter topic?
Why remove identifiers before Iceberg?
What does Flink aggregate here?
Tweak it with chat, export PNG/SVG, or fork it for your own use case.