Marmot is a single Go binary backed by Postgres. That's it!
It already supports: Full-text search across tables, topics, queues, buckets, APIs Glossary and asset to term associations
Flexible API so it can support almost any data asset!
Terraform/Pulumi/CLI for managing a catalog-as-code
10+ Plugins (and growing)
Live demo: https://demo.marmotdata.io
What we missed on marmot was existing integrations with Airflow and other plugins like Tableau, PowerBI etc as well as other features such as sso, mcp etc.
We're an enterprise and needed a more mature product. Fingers crossed marmot reaches there soon.
SSO is sort kind of available, but undocumented, it currently only supports Okta but I'm working on fleshing out a lot of this in the next big release (along with MCP)
Also, thanks for pointing out the issue with the docs, I'll get that fixed!
Also, what key decisions do other data catalogs make via your choices? What led to those decisions and what is the benefit to users?
I like to think of Marmot as more of "operational" catalog with more of a focus on usability for individual contributors and not just data engineers. The key focus being on simplicity, in terms of both deployments and usability.
Versus the conceptually simpler "one binary, one container, one storage volume/database" model.
I acknowledge it's a false choice and a semi-silly thing to fixate on (how do you perf-tune ingestion queue problems vs write problems vs read problems for a go binary?)..
But, like, I have 10 different systems I'm already debugging.
Adding another one like a data catalog that is supposed to make life easier and discovering I now have 5-subsystems-in-a-trenchcoat to possibly need to debug means I'm spending even more time on babysitting the metadata manager rather than doing data engineering _for the business_