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4. Build the data model

Step 4 of 5 — setup step 4, The data model. At the end of it, Cube is serving a model of the tables you picked, and the portal opens on Chat.

What you are building

A semantic layer is a description of your data that a computer can check: which tables matter, what each column means, how tables join, which numbers can be added up (measures) and which values you group and filter by (dimensions). Every question is answered through it, so the agent can only use joins and formulas the model defines.

Pick the tables

127.0.0.1:8420/
Schemapublic ▾Filter 7 tablesAllNoneFactsRe-read
fact_daily_salesfact9 cols · → dim_stores, dim_items
dim_storesreferenced6 cols
dim_itemsreferenced7 cols
fact_stock_dailyfact6 cols · → dim_stores, dim_items
fact_transfersfact8 cols · → dim_stores, dim_items
dim_calendar5 cols
staging_importno key12 cols
3 of 7 tables — including dim_stores, dim_items because your selection references themAlso write descriptions and views with AIBuild the model from 3 tables

One fact table ticked; the two dimension tables it joins to come with it.

  • Schema selects which schema to list. Filter narrows the list.
  • All, None and Facts select quickly. Facts are tables nothing points at — where additive numbers usually live.
  • Tables your selection points at through foreign keys are ticked for you: Included because your selection references it.
  • The footer warns about joins that will be dropped, and tables with no key that nothing can join to.
  • Re-read picks up a schema change in the warehouse.

Tick Also write descriptions and views with AI to run all four steps. Without it the model is cubes only — no descriptions and no views — and the agent has less to go on.

Click Build the model from N tables. A progress panel shows each step, the table it is on, and a log. Cancel stops it.

If Describe or Views fails, the cubes are already built and serving. You can carry on and ask the model editor's assistant for descriptions and views instead.

What the build does

Step What happens
Read the database Reads every schema and table, with keys and foreign keys. Runs on its own when you arrive. Nothing is written to the model
Build cubes Each table you picked becomes a cube, with its keys and joins
Describe with AI The AI writes a description of each table and column, and suggests measures. One model call per table — the slow part
Build views Builds views: clean, joined surfaces for questions, grouped into folders

The AI returns decisions, never files. The portal writes the files, and Cube is the judge: each build goes to a temporary branch first, and only if Cube compiles it does it replace what is served. If Cube refuses, the message says what failed and nothing was replaced.

The words

Word Means Example
Cube One business thing, from one table fact_daily_sales
Measure A number you can add up or count total_revenue
Dimension A value you group or filter by region, sale_date
Join How two cubes relate a sale belongs to a store
View A joined surface the agent asks questions against daily_sales = sales + stores + items
Folder A group of members inside a view "Store", "Money"
Segment A named, reusable filter orders.paid_only

When the build finishes, the portal opens on Chat.

Next: 5. Ask your first question