# Redshift
source: https://docs.chalk.ai/docs/redshift

## Integrate with SQL data sources.

Chalk supports Redshift
as a SQL source.
You can configure the Redshift-specific
options using the RedshiftSource init args,
or configure the source through your dashboard, and
reference the source in your code.

### Adding Redshift

By navigating to Integrations > Add a data source and
selecting Redshift, you'll find a form where you can input information about your Redshift integration.

As part of the Redshift configuration, you will be prompted to also provide an IAM role and a
S3 bucket. The IAM role would be assumed by Redshift to do basic operations (get, put, list objects)
in the S3 bucket provided. The S3 bucket itself does not require versioning, but you can
set a retention policy on the bucket. The retention policy should be somewhere from 1 - 14 days,
to balance storage efficiency with history that can be useful in debugging.

### Integrations Setup

After configuring your Redshift integration in the dashboard, define your data sources in Python:

```
from chalk.sql import RedshiftSource

risk = RedshiftSource(name="RISK")
marketing = RedshiftSource(name="MARKETING")
```

Then reference them in SQL file resolvers using the name parameter. For example, to query from the RISK source:

```
-- type: online
-- resolves: User
-- source: RISK
SELECT id, credit_score FROM users
```

And to query from the MARKETING source:

```
-- type: online
-- resolves: User
-- source: MARKETING
SELECT id, email, campaign_status FROM users
```