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LinkedIn Databus

Free#20 of 22 in Change Data Capture Software

LinkedIn Databus: A specialized self-hosted CDC system for ordered, replayable Oracle and MySQL events. Ranked #20 of 22 in Change Data Capture Software by our editors (6.9/10); pricing: Open source; best for low-latency Oracle or MySQL event transport.

6.9/10Editor score

Try LinkedIn Databus

LinkedIn Databus6.9 Try LinkedIn Databus

At a glance

Editor score
6.9 / 10
Pricing
Open source
Best for
Low-latency Oracle or MySQL event transport
Paid from
Free
Deployment model
Self-hosted
Facts checked
20 Sep 2026

Where it wins
- Low-latency transport with ordered, at-least-once delivery
- Replay, bootstrap, catch-up, and arbitrary-time consumption
- Server-side filtering and partitioned consumption
Where it doesn't
- Source coverage focuses on Oracle and MySQL
- The MySQL binlog adapter is described as a proof of concept
- Self-hosted deployment requires engineering ownership

Our verdict on LinkedIn Databus

LinkedIn Databus is an open-source, self-hosted change data capture system for engineering teams that need to capture database changes and transport them to downstream applications, indexes, caches, and other derived systems. It uses database log mining and relay components to deliver ordered change events, with Oracle integration and a MySQL binlog source adapter. The product fits teams building low-latency data pipelines around these databases, especially when replay, bootstrap, catch-up, filtering, and partitioned consumption are important requirements.

Its strongest functional area is event handling after capture. Databus supports ordered and at-least-once delivery, replay, arbitrary-time consumption, full bootstrap and catch-up, partitioned consumption, and server-side filtering. The relay and client architecture gives teams a defined path for moving changes from primary data stores to downstream consumers. These capabilities make it suited to systems where consumers need consistent event flow and the ability to recover or start from a chosen point rather than only receive new changes.

The trade-off is specialization and ownership. Oracle is a supported source, while the MySQL binlog adapter is described as a proof of concept, so teams should evaluate that adapter carefully against their deployment requirements. The self-hosted model also places deployment and ongoing operation with the adopting engineering team. LinkedIn Databus is a strong fit for teams that want open-source, log-based CDC with replay and ordered delivery for Oracle or MySQL. Teams seeking broader source coverage, a managed service, or a more current operational ecosystem should consider alternatives.

Your product? Our editors can test LinkedIn Databus in depth and publish a review, labelled as paid · from $149/mo →

LinkedIn Databus pricing

Plans Open sourceFree Free to use — no paid tier required for the core job.

See plans on github.com

LinkedIn Databus fact sheet

Free plan	Not verified
Paid from	Free
Deployment model	Self-hosted
Capture method	Log-based
Source connectors	Not verified
Destination connectors	Not verified
Schema evolution	Not verified
Initial snapshot	Yes
Deployment	Self-hosted
Built for	Mid-market, Enterprise (editorial estimate)
Integrations	2 integrations: Oracle Database, MySQL
Pricing	Open source
Website	github.com
Facts checked	20 Sep 2026

LinkedIn Databus integrations

LinkedIn Databus lists 2 integrations on its own site.

Oracle Database
MySQL

Alternatives to LinkedIn Databus

See all LinkedIn Databus alternatives →

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Featured on iTechGuides

LinkedIn Databus is listed in our Change Data Capture Software directory. Add the badge to your site — it links back to this page.

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Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026

Advertiser disclosure: iTechGuides is reader-supported. Vendors can pay for top positions in our rankings and for a place on other products' pages, and we may earn a commission when you click some links. Bought places stand at the number bought; this page and our disclosure say so. Paying vendors may get a fuller, more detailed review from our editors, and that review can change their score. How we rank.

Last updated 7 Oct 2026 · How we research and update