> ## Documentation Index
> Fetch the complete documentation index at: https://leylandscompany.leylnd.me/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.
</Warning>

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.

<Tabs>
  <Tab title="ClickHouse">
    <Steps>
      <Step>
        #### Add a Data Lake Catalog

        In ClickHouse Cloud, go to **Data Sources → Add → Data Lake Catalog → AWS Glue**.

        <Frame>
          <img src="https://mintcdn.com/leylandscompany/90P_ewflNoopxCjY/assets/lakehouse/ch-add-data-source.png?fit=max&auto=format&n=90P_ewflNoopxCjY&q=85&s=f285f46b21de9bfc49c40cf85343cbf8" alt="ClickHouse Cloud Data Sources page with the Add button" width="738" height="762" data-path="assets/lakehouse/ch-add-data-source.png" />
        </Frame>
      </Step>

      <Step>
        #### Configure the catalog

        Set the **region** to `us-east-2`, then paste the **access key** and **secret** Autumn sent you.

        <Frame>
          <img src="https://mintcdn.com/leylandscompany/90P_ewflNoopxCjY/assets/lakehouse/ch-add-catalog.png?fit=max&auto=format&n=90P_ewflNoopxCjY&q=85&s=828096130b2ad951426f28a06a13b89f" alt="Selecting Data Lake Catalog then AWS Glue as the source type" width="1450" height="1746" data-path="assets/lakehouse/ch-add-catalog.png" />
        </Frame>
      </Step>

      <Step>
        #### Save and browse

        Save the connection. The catalog mounts as a database, and your tables appear as `` `<catalog>`.`<namespace>.v2_3_<object>` ``.

        <Frame>
          <img src="https://mintcdn.com/leylandscompany/90P_ewflNoopxCjY/assets/lakehouse/ch-catalog-tree.png?fit=max&auto=format&n=90P_ewflNoopxCjY&q=85&s=f390c8c38f64fa23d24fdcc00bca28d8" alt="Mounted catalog tree showing the v2_3_* tables" width="586" height="944" data-path="assets/lakehouse/ch-catalog-tree.png" />
        </Frame>

        <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](/documentation/lakehouse/querying#identifier-syntax).
        </Note>
      </Step>
    </Steps>
  </Tab>

  <Tab title="BigQuery">
    <Steps>
      <Step>
        #### 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>

      <Step>
        #### 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](/documentation/lakehouse/querying#identifier-syntax).
        </Note>
      </Step>
    </Steps>
  </Tab>
</Tabs>

## 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(*)
```

<Frame>
  <img src="https://mintcdn.com/leylandscompany/90P_ewflNoopxCjY/assets/lakehouse/test-connection.png?fit=max&auto=format&n=90P_ewflNoopxCjY&q=85&s=f3220cccfd59f1fbe3e34f9c6f00808f" alt="Successful row-count result in the query console" width="1950" height="752" data-path="assets/lakehouse/test-connection.png" />
</Frame>

Once that returns, head to the [Schema Reference](/documentation/lakehouse/schema) to see what's available, then [Querying](/documentation/lakehouse/querying) 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.