linkedin/databus

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

3679 stars · 737 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

Databus is LinkedIn's source-agnostic distributed change data capture (CDC) system that reliably captures and streams primary data store changes to derived stores, caches, and indexes. It provides low-millisecond latency transport with thousands of events per second per server, infinite look-back, and rich subscription functionality.

Use cases

• capture database changes and stream them to search indexes
• invalidate cache entries when primary data changes
• replicate OLTP database changes to derived data stores
• replace dual-write patterns with a reliable change pipeline
• build a scalable event pipeline from transaction logs
• feed downstream data processing systems with primary store mutations

When to choose

• you need reliable change data capture from primary databases into derived stores
• you want to avoid consistency problems of application-driven dual writes
• you need low-latency event transport with infinite look-back and subscriptions
• you run a JVM-based data infrastructure at large scale

When to avoid

• you need a modern actively developed CDC tool with broad database connector support
• your stack is not JVM-based
• you want a simpler single-node change capture solution
• you need turnkey support for databases beyond Oracle and MySQL

Facets

library · maturity maintenance

streaming etl message-queue databases microservices big-data jvm cross-platform change-data-capture cdc data-pipeline event-streaming linkedin log-mining replication data-engineering linux

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markdown · JSON · MCP: product_card(name="linkedin/databus")