> ## Documentation Index
> Fetch the complete documentation index at: https://www.dataframer.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Databricks integration

> Overview of the DataFramer - Databricks integration. DataFramer is a Validated Databricks Partner.

DataFramer runs on the models your own Databricks workspace serves, so your data never reaches another provider's models. Two notebooks also read a sample of your data out of Unity Catalog, generate a larger dataset that keeps its structure and variation, and write the result back.

## Running DataFramer on your Databricks models

A company admin saves a Databricks service principal in the DataFramer web app under **Settings > Keys**, provider **Databricks**: client ID, client secret, and the workspace URL. Databricks credentials need an Enterprise plan.

DataFramer calls your models through the workspace's Unity AI Gateway. Each model's DataFramer id names its `system.ai` model service: `databricks/system.ai.claude-sonnet-5` is `system.ai.claude-sonnet-5`. The service principal needs `EXECUTE` on those model services, which Databricks grants to all account users by default.

Every DataFramer feature that calls a model then calls yours — discovery and tracking, root causes and remediation, calibrated judges, evaluations, API guardrails, Review Copilot, anonymization and data generation — and your Databricks account is billed for those calls.

A company holds one model-provider credential at a time, so saving this one replaces an Anthropic or OpenAI key for everyone in it. Model pickers then offer only what your workspace serves: a model chosen elsewhere is substituted for the closest one it serves, and one it has no equivalent for is refused.

## Generating data from Unity Catalog

Both workflows are notebooks you import into your Databricks workspace and run there. The single-file one can also run in Google Colab or a local Jupyter; the multi-file one reads and writes files in a volume, so it cannot.

The notebook needs no Databricks credentials of its own. It authenticates as whoever runs it, and reads your DataFramer API key from a Databricks secret scope. Each workflow page lists the permissions that runner needs.

| | [Single file](./databricks-single-file) | [Multi-file and multi-folder](./databricks-multi-file) |
| - | - | - |
| **A generated sample is** | one row of a single file | several files, or a folder of them |
| **Seed data comes from** | a Unity Catalog table | a ZIP archive in a volume |
| **Results are written to** | a Unity Catalog table | files in a volume |


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