Multi-Tenant Streaming Architectures: Design Patterns

Streaming data governance ensures data flowing through real-time pipelines is well-defined (schema registry), traceable (lineage), and controlled (access policies). Without governance, streaming pipelines become black boxes where no one knows what data flows where.

Three Pillars of Streaming Governance

1. Schema Registry

Track and enforce data schemas for Kafka topics and streaming sources:

Confluent Schema Registry (Avro, Protobuf, JSON Schema)
AWS Glue Schema Registry
Enforce backward/forward compatibility on schema changes

2. Data Lineage

Trace data from source to destination through transformations:

Which materialized views depend on which sources?
If a source changes, what downstream views are affected?
RisingWave's cascading materialized views provide implicit lineage

3. Access Control

Control who can read which streams and views:

PostgreSQL GRANT/REVOKE on RisingWave views
Kafka ACLs on topics
Iceberg catalog-level permissions

Frequently Asked Questions

How do I track lineage in streaming pipelines?

In RisingWave, materialized view definitions explicitly reference their source tables and upstream views — providing SQL-level lineage. For cross-system lineage (Kafka → RisingWave → Iceberg), use metadata catalogs or lineage tools like OpenLineage.

Streaming Data Governance: Schema Registry, Lineage, and Access Control