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

# Connecting

> Attach ClickHouse or BigQuery to your Autumn Lakehouse catalog.

> [!WARNING]
>
> **Available on request** — The Autumn Lakehouse is provisioned per customer. Contact us at [hey@useautumn.com](mailto:hey@useautumn.com) to get access. Any customer may use our [SQL endpoint](https://docs.useautumn.com/documentation/query-with-sql.md) instead.

When your Lakehouse is provisioned, Autumn privately sends you:

* Your **catalog** and **namespace** names (the namespace is where your `v2_3_*` tables live).
* A **scoped access key and secret** for the underlying object storage.
* The **region**: `us-east-2`.

Keep these credentials private. If you're connecting **BigQuery**, you get a **project** and **dataset** instead of a catalog, namespace, and storage key — Autumn owns the external connection, so there are no object-storage credentials for you to hold. Then connect your engine below.

> [tab] ClickHouse
>
> **Step 1**
>
> #### Add a Data Lake Catalog
>
> In ClickHouse Cloud, go to **Data Sources → Add → Data Lake Catalog → AWS Glue**.
>
> ![ClickHouse Cloud Data Sources page with the Add button](https://mintcdn.com/autumn-b9b4c0fb/upS3Dj1rCWylIZHt/assets/lakehouse/ch-add-data-source.png?fit=max\&auto=format\&n=upS3Dj1rCWylIZHt\&q=85\&s=71b36c431f01135f13ad4a7c43723d1c)
>
> **Step 2**
>
> #### Configure the catalog
>
> Set the **region** to `us-east-2`, then paste the **access key** and **secret** Autumn sent you.
>
> ![Selecting Data Lake Catalog then AWS Glue as the source type](https://mintcdn.com/autumn-b9b4c0fb/upS3Dj1rCWylIZHt/assets/lakehouse/ch-add-catalog.png?fit=max\&auto=format\&n=upS3Dj1rCWylIZHt\&q=85\&s=bffd82298d60e7a67d93e36496d4d61d)
>
> **Step 3**
>
> #### Save and browse
>
> Save the connection. The catalog mounts as a database, and your tables appear as `` `<catalog>`.`<namespace>.v2_3_<object>` ``.
>
> ![Mounted catalog tree showing the v2\_3\_\* tables](https://mintcdn.com/autumn-b9b4c0fb/upS3Dj1rCWylIZHt/assets/lakehouse/ch-catalog-tree.png?fit=max\&auto=format\&n=upS3Dj1rCWylIZHt\&q=85\&s=e44b69e5ae80a067548f4a6340420402)
>
> > [!NOTE]
> >
> > The whole `<namespace>.v2_3_<object>` is the **table name** — the dot is literal, not a database separator. You must backtick both parts when querying. See [Querying](https://docs.useautumn.com/documentation/lakehouse/querying.md#identifier-syntax).

> [tab] BigQuery
>
> **Step 1**
>
> #### Access the dataset
>
> Autumn provisions your Iceberg tables as BigLake external tables in a BigQuery dataset you're granted access to. We share the **project** and **dataset** names with you during onboarding — you don't create the external connection yourself.
>
> **Step 2**
>
> #### Verify access
>
> List the dataset to confirm the tables are visible. They appear as `` `<project>.<dataset>.v2_3_<object>` ``, plus the unversioned `events` table.
>
> ```sql theme={null}
> SELECT table_name
> FROM `<project>.<dataset>.INFORMATION_SCHEMA.TABLES`
> ORDER BY table_name;
> ```
>
> > [!NOTE]
> >
> > Unlike ClickHouse, BigQuery addressing is ordinary three-part `project.dataset.table` — there is no literal dot inside the table name. See [Querying](https://docs.useautumn.com/documentation/lakehouse/querying.md#identifier-syntax).

## Test the connection

Run a quick count against any table to confirm everything is wired up:

```sql theme={null}
SELECT count() FROM <your v2_3_customers table>;   -- BigQuery: COUNT(*)
```

![Successful row-count result in the query console](https://mintcdn.com/autumn-b9b4c0fb/upS3Dj1rCWylIZHt/assets/lakehouse/test-connection.png?fit=max\&auto=format\&n=upS3Dj1rCWylIZHt\&q=85\&s=510431dafb9e2f053fc565c70d13602d)

Once that returns, head to the [Schema Reference](https://docs.useautumn.com/documentation/lakehouse/schema.md) to see what's available, then [Querying](https://docs.useautumn.com/documentation/lakehouse/querying.md) for examples.
If you notice the number seems lower than expected, it's because it may take a few minutes to a few hours for your data warehouse to catch up.


This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.