> For the complete documentation index, see [llms.txt](https://docs.jetadmin.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.jetadmin.io/user-guide/synced-tables/join-data-from-multiple-sources.md).

# Join data from multiple sources

Join synced Orders and Customers data in a read-only Virtual Collection.

To demonstrate how the data blending works, we'll use two data sources: Airtable and Google Sheets, where the former contains the `Order` table and the latter - the `Customers` table. Notice that the `Customer ID` column in the `Orders` table refers to the `ID` in the `Customers` table.

![](https://3448227606-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-LQ08RFAKZvFADEiXKFy%2Fuploads%2FS9RdU4tCDHx2B0RKyb7n%2Fdtzhy.png?alt=media\&token=8f83cd64-df0a-435c-b7dd-166a2c5464b6)

Once you've connected your data sources (read more on particular integrations [**here**](/user-guide/data-sources.md)), proceed to the data section and pick a data source where you want to perform the blending *(you can choose either one, it's just a matter of convenience)*

![](https://3448227606-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-LQ08RFAKZvFADEiXKFy%2Fuploads%2FRdrQQSriYqs7ggBKpTWB%2Fdhtffyu.png?alt=media\&token=20ca9b10-abc1-49a0-8a08-c8404f41f9cb)

1. Create a new **Virtual Collection**
2. Name the Collection
3. Click on the `Create` button
4. You'll see all the data sources you've connected using "Sync" on the right. You can now pick the columns and use them in your query:

{% @arcade/embed url="<https://app.arcade.software/share/fHlP2za57Nst2y8C1fW6>" flowId="fHlP2za57Nst2y8C1fW6" %}

And after writing and running our query, we get the resulting table, containing `Full name` and `Country` columns from the `Customers` table together with the `Order Date` column from the `Orders` table.

![](https://3448227606-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-LQ08RFAKZvFADEiXKFy%2Fuploads%2Ft1tiQ3tDc1WlCe4A3YCp%2Frdzhxgtd.png?alt=media\&token=55bd9623-8acd-48f2-b6c1-d10c7b44f334)

After saving changes, we get a collection with joined data that we can later use in the interface.

## Verify the join

Use a stable customer ID as the matching key. Two orders with the same customer ID should show the same customer. Check a missing customer ID and decide whether your query should keep or exclude that order.

For a small test, use Customers 1 (Ada) and 2 (Grace), with Orders 101 and 102 linked to customer 1 and Order 103 linked to customer 2. A join should return three order rows. Unexpected extra rows can mean the customer key is not unique.

A Virtual Collection supports SELECT queries and is read-only. It does not write the joined result back to either source.

## Related example: a join within one Jet Tables resource

The following SQL collection joins Tickets and Customers from the **same** Jet Tables resource. It illustrates matching IDs and testing a parameterized join; it is not a cross-source Virtual Collection example.

<figure><img src="https://3448227606-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-LQ08RFAKZvFADEiXKFy%2Fuploads%2FnLtNnGpSSgokf5yApt63%2FS23-ticket-customer-join-query.jpg?alt=media" alt="Parameterized SQL joining Tickets and Customers in Jet Tables"><figcaption><p>Join the two demo tables and filter by the numeric customer_id input. The text cast matches this demo link field's stored type.</p></figcaption></figure>

<figure><img src="https://3448227606-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-LQ08RFAKZvFADEiXKFy%2Fuploads%2F9l93O0Pf4dk6Xtuxy4Bx%2FS24-ticket-customer-join-result.jpg?alt=media" alt="Successful result linking ticket 1 to Ada Gazey"><figcaption><p>With customer_id set to 5, the query returns ticket 1 and its linked customer, Ada Gazey.</p></figcaption></figure>


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