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

> ## Agent Instructions
> These docs contain multiple Speckle product experiences. For Speckle 2026.9, prefer /next/ pages for the topics and changes they cover. The navigation label Current does not override this version-specific precedence.
> Coverage under /next/ is incremental. Use Current documentation for unchanged topics and when a /next/ page says coverage is unavailable or explicitly refers you to Current. Do not infer a feature was removed from a missing page.
> Read the page-specific documentation status, applicability, affected guidance and replacement links in agent-only content. Impacted, changed and superseded apply only to the stated scope; they do not mean the entire feature is deprecated. Historical connector guides apply only to the legacy connector described.
> Match guidance to the customer's deployment version, model data format and connector. Respect plan, permission and compatibility restrictions. If an unknown version or data format changes the answer, ask a focused clarification; do not infer deployment version from the date alone.

# Databricks

> Register read-only Databricks tables as data sources in Speckle for the AI chat and data tools.

<Badge>Beta</Badge>

<Note>
  The Databricks integration is in **Beta** and is enabled per workspace. Before you can register
  tables, an IT administrator must prepare Databricks and connect the SQL warehouse in Speckle. If
  your project's **Integrations** does not show Databricks, ask your IT administrator to follow
  [Enable Databricks integration](/it-admin/integrations/databricks).
</Note>

## Setup

These steps assume an IT administrator has already connected a Databricks SQL warehouse in Speckle. If it hasn't, your IT administrator must complete [Enable Databricks integration](/it-admin/integrations/databricks) first.

<Steps>
  <Step title="Browse to your table">
    In Speckle, open your project's **Integrations > Databricks**, expand **catalog > schema**, and find the table you want. Only catalogs, schemas, and tables your IT administrator has granted access to will appear. Select a table to preview its rows.

    <Frame>
      <img src="https://mintcdn.com/speckle/WshrmWLtUwGFa3Rl/images/connectors/data-warehouses/databricks_preview_table.png?fit=max&auto=format&n=WshrmWLtUwGFa3Rl&q=85&s=ee6a59e8e8a0389d0bb503a473d03b4a" alt="Browsing and previewing a Databricks table" width="3452" height="1770" data-path="images/connectors/data-warehouses/databricks_preview_table.png" />
    </Frame>

    You can preview the table rows before registering.
  </Step>

  <Step title="Choose the columns and register">
    Pick the **columns** to include — only the columns you choose are ever read or cached — then select **Register**. Speckle validates access, reads the selected columns, and builds the first snapshot. The table now appears as a registered data source.

    <Frame>
      <img src="https://mintcdn.com/speckle/WshrmWLtUwGFa3Rl/images/connectors/data-warehouses/databricks_register_table.png?fit=max&auto=format&n=WshrmWLtUwGFa3Rl&q=85&s=d48e094e5d7b326e869578b6f0f69b27" alt="Registering a Databricks table as a data source" width="1558" height="1254" data-path="images/connectors/data-warehouses/databricks_register_table.png" />
    </Frame>

    The table appears as a registered data source in your project.
  </Step>
</Steps>

Registered data sources are available to Speckle tools such as the **AI chat**, where you can query the table and combine it with your model data. See [Intelligence Dashboards](/analytics/intelligence-dashboards) for analytics and data tools. Help shape this workflow on the [Databricks integration study](/get-involved/user-testing#databricks).

## How snapshots are stored and refreshed

When you register a table, Speckle reads the columns you selected and stores a cached **snapshot** in your workspace's object storage. Tools query the snapshot rather than your live warehouse.

* **Automatic refresh.** A snapshot is rebuilt from the warehouse when it goes stale (default freshness window: 24 hours), the next time it's used.
* **Manual refresh.** You can force an immediate refresh from the registered table's controls — useful right after the source data changes.
* **Disconnecting.** If your IT administrator removes the Databricks connection, registered sources keep serving their **last snapshot read-only**. Reconnecting the same warehouse relinks them and resumes refreshing.

## FAQ

<AccordionGroup>
  <Accordion title="Which columns does Speckle read?">
    Only the columns you select when registering a table. Columns you don't include are never
    queried or cached.
  </Accordion>

  <Accordion title="Is the data live or cached?">
    Speckle stores a snapshot of your registered columns and refreshes it on a schedule. See [How
    snapshots are stored and refreshed](#how-snapshots-are-stored-and-refreshed).
  </Accordion>

  <Accordion title="What happens if the Databricks connection is removed?">
    Your registered sources keep serving their last cached snapshot read-only, so existing tools
    don't break. If your IT administrator reconnects the same warehouse, refreshing resumes.
  </Accordion>

  <Accordion title="Can Speckle modify or write to my warehouse?">
    No. The integration is **read-only** — Speckle issues `SELECT` queries only and never writes,
    updates, or deletes data in Databricks.
  </Accordion>
</AccordionGroup>


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