Blank Databricks Unity Catalog project
Three SQL-editor queries to a governed lakehouse — no credentials, ever.
Data EngineerRelated use →
Lakehousevendor
Connect Databricks
Give Unity Catalog something to check thousands of columns against.
How CoreModels works with Databricks
A mid-size Databricks estate is impressive on paper and exhausting in practice: three-level namespaces, thousands of columns, comments wherever someone bothered. The questions that eat the week — may this column change, who consumes it, is documentation real — are not catalog questions.
CoreModels imports the estate, records the first audit, and uses later extracts as drift candidates. Lineage-aware governance ties producers to consumers so a type change is not a local edit. Generate-back and the CI gate put the check in the same place code already reviews.
This is how a lakehouse gets a contract without pretending the catalog was one.
Recipes
Recipes
Recipes for Databricks
CI Drift Gate for Unity Catalog
Wire a Unity Catalog drift gate into CI — and keep it honest after merge.
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Delta DDL back to Databricks
Governed model in, coremodels_delta_tables.sql out.
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First governed Unity Catalog import
One information_schema extract to a governed lakehouse and a first audit.
Data EngineerRelated use →
Lineage-aware lakehouse governance
Govern tables with the dependency graph that explains them.
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Uses
Problems this connector fixes
Articles that explain the gap, then point at the recipe that closes it.
Problem
Six Thousand Columns, and Nothing to Check Them Against
Run one query against `system.information_schema` in a mid-size Unity Catalog estate and what comes back is genuinely impressive: every table and view addressed as `catalog.schema.table`; every column with its `full_data_type` spelled out exactly as the catalog holds it, its ordinal position, its nullability flag, and a comment wherever somebody bothered. Hundreds of tables. Thousands of columns. Complete, accurate, current.
OutcomesThe Meeting That Used to Take Two Weeks
Somebody proposes changing a column. Not a dramatic change — `order_total` needs a different type after a rounding bug, or `status` needs one more allowed value.
Recipe: First governed Unity Catalog import
GovernanceTrust Is a Set of Powers Refused
Most tools that want to govern a data platform begin by asking for access to it. A service principal, a warehouse role, a token with just enough scope — and from that moment the honest answer to "what can this thing do to my estate?" is "you'd have to read the code."
Recipe: Delta DDL back to Databricks
EcosystemFollow One Table Across Your Stack
Pick a table. Say `sales.orders`.
Recipe: Lineage-aware lakehouse governance
AgentsWhat an Agent Should Be Allowed to Assume
Hand an AI agent a table name and a task, and before it writes a line it has made a dozen assumptions. That `customer_id` identifies a customer and is unique. That `status` has a small set of values and `active` is one. That `amount` is a number, in one currency, never null. That the `orders` it found is *the* `orders`. That today's shape is the shape it was told about.
QuickstartZero to First Audit: Governing a Databricks Unity Catalog Estate with CoreModels
This is a working session, not a tour. By the end of it you will have a governed model of a Unity Catalog schema inside CoreModels, and you will have run your first drift audit against it — without installing anything in your workspace and without handing us a single credential. Everything CoreModels learns about your lakehouse arrives as JSON files you extract yourself from the Databricks SQL editor.
Recipe: Blank Databricks Unity Catalog project · First governed Unity Catalog import
APIEvery Endpoint of the CoreModels Databricks Integration, With Real Payloads
Reference articles usually show the happy path and gesture at the rest. This one is the full inventory: every HTTP route the CoreModels Databricks Unity Catalog connector answers on, the role each one enforces, the request and response bodies as they actually are, and — where a verb lives on one surface but not the other — an explicit statement of that fact rather than a diagram that implies otherwise.
MCPAsk Your Agent Whether the Lakehouse Drifted: Unity Catalog Governance over MCP
"Has our Unity Catalog estate drifted from what we agreed it means?" is a question an AI agent can now answer with evidence instead of vibes. The same governance verbs that back the CoreModels HTTP API — status, audit, import, generate — are exposed as tools on our MCP server, so an agent can inspect a governed Databricks estate, audit a fresh extract against it, and draft the fix, all inside one conversation. This article walks that loop exactly as an agent drives it, with the real tool names and arguments, including the `artifactUrls` path for extracts too large to paste into a chat.
AutomationFail the Build, Not the Dashboard: A CI Drift Gate for Databricks Unity Catalog
Somewhere in your lakehouse, a notebook is about to rebuild a table with a widened column type, and nothing will fail until a dashboard does — days later, in front of the wrong audience. The cheapest place to catch that is a pull request. This article turns the CoreModels schema audit into a merge-blocking CI gate for a Unity Catalog estate, then builds out the rest of the operational loop most drift setups forget: the status badge, the rolling history, the one-call re-audit for when the governed model itself changes, and the scheduled heartbeat that keeps watch between releases.
Recipe: CI Drift Gate for Unity Catalog
Deep diveAnatomy of an Import: How a Unity Catalog Estate Becomes a Governed Graph
Take a single row of a Unity Catalog extract:
Recipe: Delta DDL back to Databricks · Lineage-aware lakehouse governance