Data Modeling

When every report carries its own copy of the logic, numbers drift between teams and tools. A Data Mart is that logic defined once by the analyst — SQL, Table, View, Pattern, Connector or Joinable — with an output schema that says what each field means.

• Every report built on a mart shows the SQL behind its numbers, re-runnable in your own warehouse.
• Nothing reaches a spreadsheet, a dashboard or an AI assistant until the analyst publishes the mart.
• Start from a query, table, view or pattern you already have; the AI helper drafts field names and descriptions.

A business user or an AI assistant can only use a Data Mart it understands, and metadata typed by hand is the task every analyst defers. Metadata Autofill drafts it for the analyst to approve.

• Every draft is written from the schema and a sample of up to 30 real rows, so it describes what the data actually contains.
• Autofill drafts, the analyst edits and saves — the descriptions an AI answer is grounded in stay the analyst's.
• One click fills aliases and descriptions for every field and the Data Mart's own title and description; field descriptions reach the spreadsheet's column picker and header-cell notes.