Create an artifact

Produce documentation and DDL from your models.

What you’re building

Artifacts are how Snap Data Studio delivers curated context to the people, agents, and systems that use it. You capture business meaning in conceptual, logical, and physical models; artifacts turn that approved context into consumable outputs — human-readable documentation, database DDL, and other open-standard files downstream consumers can retrieve when they need them.

Snap Data Studio remains the place to author, review, and govern what the business means. Artifacts are the delivery layer: a governed source of truth so coding agents, platforms, catalogs, and teams start from the same context instead of rediscovering or reinterpreting it for each task.

What you can create

Artifact	Format	When to use
Documentation	Markdown	Explain the model for people: scope, definitions, design decisions, and how tables map to business meaning.
DDL	SQL	Produce warehouse-ready CREATE scripts from a physical diagram that has a platform warehouse configured (Snowflake or Databricks).

Each artifact type has its own entry point — see Docs and DDL below. Some flows also create documentation for you: Generate Model adds AI-Generated Documentation and, when applicable, Column Mappings. Reverse engineering and warehouse conversion can add reports as well.

Docs

Create a blank markdown document from the diagram, write with Copilot, or use docs that generation already produced.

Add documentation

1. Open Diagrams in the project sidebar.
2. Hover the target diagram and open its ⋯ menu.
3. Click Add documentation.

Snap Data Studio opens an Untitled Documentation tab nested under that diagram, with a starter markdown body. Use Edit and View to switch modes. Back to model returns to the parent diagram. Edits autosave.

From the documentation item’s ⋯ menu you can Rename, Duplicate, or Delete it.

Write docs with Copilot

With the diagram open, use AI Copilot in Agent mode and ask for documentation. Copilot stages a markdown draft (it does not save until you accept). Review the changes, edit if needed, then Keep / Accept to persist or Revert / Reject to discard.

Docs from Generate Model

When you generate a Kimball or Data Vault model, the new diagram includes AI-Generated Documentation and often Column Mappings. Open those children from the Diagrams drawer to review or edit them like any other documentation artifact.

DDL

Forward engineering turns a saved physical diagram into a SQL script for its configured warehouse. The script is stored as a DDL artifact under the diagram; Snap Data Studio does not run it against the warehouse.

Forward engineer

1. Open Diagrams in the project sidebar.
2. Hover the target diagram and open its ⋯ menu.
3. Click Forward engineer.
4. In the Forward Engineer modal, confirm the database type for the diagram’s warehouse (Snowflake or Databricks).
5. Click Generate DDL.

While generation runs you see progress (and can Cancel generation). On success, a code editor tab opens under the diagram with a .sql file named from the diagram and dialect (for example Sales_Model_snowflake.sql). The SQL is editable and autosaves.

What the script includes

• Table and column definitions, primary keys, unique constraints, nullability, and comments
• Foreign keys inline where the dialect and model allow
• Snowflake: CREATE OR ALTER TABLE
• Databricks: CREATE TABLE

Prerequisites

• The diagram must have a warehouse configured in physical location settings so physical mode is available
• The diagram must be saved (dirty open diagrams are autosaved first when possible)
• The diagram must contain at least one table

What’s next

• Create a conceptual model
• Create a logical & physical model