Metagem and Databricks
Databricks is one platform for all your data and AI work, run by your data engineers. Metagem captures the definitions and rules buried in SharePoint, Confluence and other documents, and turns them into something your business owners can approve.
DatabricksToolkitYour engineers build metric views, definitions and checks in Unity Catalog
MetagemReady-madeA ready-made model you can extend, filled automatically; first results in two to three weeks
What Databricks does well
Databricks is becoming one platform for everything you do with data and AI: engineering, analytics, machine learning and agents. Unity Catalog governs what’s in it, including tables, files, models and the lineage between them. Its business semantics let your engineers define KPIs as metric views and certify the assets people should trust, and Genie answers business questions from those assets.
With a strong data engineering team, it covers a lot of ground.
DatabricksOne platformUnity Catalog
What it assumes you already have
- WriteMaintain
A team of data engineers to run it.
Unity Catalog is built for engineers. Metric views, definitions and quality checks are written in SQL or Catalog Explorer, and someone has to keep them current as the business changes.
By handKnowledge that’s already structured.
Databricks can store and secure your documents, but the definitions and business rules inside your SharePoint and Confluence pages stay there until someone writes them out by hand.
- Table late or incompleteEvery supplier has a valid VAT number
You already know which quality rules to check.
Databricks spots late or incomplete tables on its own. Checking whether your data follows your business rules, such as every supplier having a valid VAT number, means someone first has to know those rules and write them as checks.
Why we built Metagem
We built Metagem because most of the knowledge that defines your business isn’t in your data platform. It sits in procedures, policies and wikis in SharePoint and Confluence, and in the heads of a few people.
A ready-made model
Metagem is opinionated about how to capture it. It starts from a ready-made model built for master data, definitions, business rules and data quality, and you can extend it with your own entities.
Pulled out, linked to source
- DefinitionActive customer
- RuleDiscounts above 10% need approval
Filled from your sources
It fills that model from your documents and your Databricks tables, and links each item to its source. It also spots patterns in your data that point to rules nobody wrote down, so you don’t need to know every rule upfront. Then it ranks what breaks by business impact and effort.
- Conflict found5 definitions
Net revenue
Impact · HighEffort · Low
Gross sales minus discounts and returnsProposed
DecideSent to the owner
Owners decide
Its workflows put each definition and rule in front of the business owner who should decide, and record the decision. Your engineers stop being the go-between, and the model stays current without them maintaining it. You see the first results in two to three weeks.
How they work together
In production, Metagem runs inside your own cloud environment.
Your sources
Unity Catalog
Other data platforms
Documents
Your cloud environment
Your agents
Pushed into
Unity Catalog- 01
Metagem connects read-only to Unity Catalog, your other data platforms, and your documents in SharePoint, Confluence and elsewhere.
- 02
It fills its model with your definitions, business rules and data-quality rules, mapped to the Unity Catalog tables behind them.
- 03
Business owners agree on one version of each through Metagem’s workflows.
- 04
The agreed context reaches your agents through MCP. You can keep your definitions in Metagem, in Unity Catalog, or both.
Side by side
| Area | Databricks | Metagem |
|---|---|---|
| Getting started | Your engineers build metric views, definitions and checks in Unity Catalog | A ready-made model you can extend, filled automatically; first results in two to three weeks |
| See what you have | Catalogs the tables, files and models in your lakehouse, with lineage | Finds definitions and rules across your data platforms and documents, such as SharePoint and Confluence, each linked to its source |
| Agree on definitions | Your team writes and certifies them | Workflows send each definition to its business owner and record the decision |
| Fix what matters | Flags stale or incomplete tables, ranked by downstream usage | Checks data against your business rules, ranks findings by business impact and effort, and tracks each one until it’s closed |
| Share with AI | Genie answers from your certified assets and metric views | Serves agreed context to any agent that supports MCP; definitions can also live in Unity Catalog |
| Keep it current | Your engineers update definitions and checks | Re-runs on a schedule or when sources change, and proposes updates for owners to approve |
Getting started
Databricks
Your engineers build metric views, definitions and checks in Unity Catalog
Metagem
A ready-made model you can extend, filled automatically; first results in two to three weeks
See what you have
Databricks
Catalogs the tables, files and models in your lakehouse, with lineage
Metagem
Finds definitions and rules across your data platforms and documents, such as SharePoint and Confluence, each linked to its source
Agree on definitions
Databricks
Your team writes and certifies them
Metagem
Workflows send each definition to its business owner and record the decision
Fix what matters
Databricks
Flags stale or incomplete tables, ranked by downstream usage
Metagem
Checks data against your business rules, ranks findings by business impact and effort, and tracks each one until it’s closed
Share with AI
Databricks
Genie answers from your certified assets and metric views
Metagem
Serves agreed context to any agent that supports MCP; definitions can also live in Unity Catalog
Keep it current
Databricks
Your engineers update definitions and checks
Metagem
Re-runs on a schedule or when sources change, and proposes updates for owners to approve
Reviewed October 2026 against Databricks’ public documentation. Some features mentioned are in preview.
When you need which
Databricks alone is enough
When
- Your definitions and rules are already agreed
- Your engineers have time to encode and maintain them
- The knowledge you need is already in your data
Add Metagem
When
- Your rules sit in SharePoint, Confluence and people’s heads
- Business owners need to decide between versions
- Your engineering team is too small to maintain semantics by hand
Start with one domain
In two to three weeks, you’ll see its definitions, where they conflict and which fixes to start with.