Built on Databricks, governed with Unity Catalog
Sancire focuses on Databricks, so we go deep on the parts that decide whether a platform can be trusted: ownership, access, lineage and the evidence behind every AI decision.
Governance as a layer, not a project
Every engagement lands on the same layered model, so each new use case inherits the controls of the one before it.
| Layer | What it answers | On Databricks |
|---|---|---|
| Identity and access | Who can see what, and why | Unity Catalog grants, groups, row filters, column masks |
| Ownership and classification | Who is accountable for this data | Catalog and schema owners, tags |
| Lineage | Where a number came from | Unity Catalog lineage |
| Audit | Who did what, and when | system.access.audit |
| Pipelines | Is the data fresh and correct | Lakeflow, expectations |
| AI evaluation | Is the model good enough, and still good | MLflow evaluation, judges, tracing |
| AI controls | What the agent may do, and who approves | AI Gateway, Unity Catalog functions, Lakebase |
| Cost | What each team and use case costs | system.billing.usage, tags, budgets |
Moving to Databricks
Migrations start with a scan of your current estate using Lakebridge, Databricks’ migration tooling, and end with every table reconciled against the source. Modernize services
Already on Databricks
A readiness review scores your workspaces on governance, lineage, reproducibility and cost, with effort and impact for every gap. Start here
Databricks account teams
If you already work with a Databricks account team, we coordinate with them from scoping to go-live.
Talk to us about your Databricks platform
Tell us what you are working on. A named person replies within one business day.