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

# Azure SQL

> Connect Azure SQL as a data source in Datachecks.

To add Azure SQL as a data source, go to **Settings → Workspace → Integrations → Datasources** and click **New Datasource**. Select **Azure SQL** and fill in the connection parameters below, then click **Test & Save Datasource**.

## Connection parameters

| Field               | Description                                                                     |
| ------------------- | ------------------------------------------------------------------------------- |
| **Connection Name** | A label for this connection within Datachecks                                   |
| **Host**            | The server name hosting your database                                           |
| **Port**            | Default is `1433`. Specify an alternative if your server uses a different port  |
| **Username**        | The dedicated user for Datachecks                                               |
| **Password**        | The password for the Datachecks user                                            |
| **Database**        | The name of the Azure SQL database you want to connect to                       |
| **Schema**          | The schema you want to access                                                   |
| **ODBC Driver**     | The driver required to establish the connection (for example, `ODBC Driver 18`) |

## Setup

Run the following SQL on your Azure SQL instance to create a dedicated user for Datachecks.

```sql theme={null}
USE DatabaseName;

CREATE SCHEMA datachecks_tmp;

CREATE LOGIN DatachecksUser WITH PASSWORD = 'YourSecurePassword';
CREATE USER DatachecksUser FOR LOGIN DatachecksUser;

GRANT SELECT ON SCHEMA::YourSchema TO DatachecksUser;
GRANT SELECT, INSERT, UPDATE, DELETE ON SCHEMA::datachecks_tmp TO DatachecksUser;
```

The `datachecks_tmp` schema is used for intermediate processing and keeps Datachecks operations isolated from your production data.