Amazon Redshift

Requirements

• Data should be updated before each import.
• One table or dataview is required per conversion event or audience list.
• You need to have credentials for a user with permissions (including read) to access the data in Redshift.
• Your Redshift server must either be publicly accessible, or you can allowlist using the IP ranges for us-central1, us-east1, or us-west1 from this list of ranges.

Set up Redshift as a data source

Apply a use case now

The following sections walk you through a single procedure to link a data source to a use case. Click on your use case to expand the steps. To link the data source only, and apply a use case later, see Apply a use case later.

Customer Match

1. In Google Ads, open Tools > Shared library > Audience manager .
   Note: If you already created a segment and skipped adding a data source, click Tools > Data manager > Connected products > Connect source, then go to step 3.
2. Click +Create remarketing list > +Customer list.
3. Under Data source, select Connect a new data source.
   1. If you previously linked a data source from the Data manager screen that you want to use, select Select an existing source.
4. Search and select Redshift from the list of data sources.
5. Select Direct connection, then click Continue.
6. Select the data type, then click Next.
7. Enter your Redshift database location, port, username, password, and database, then click Connect. By default, Redshift uses database location URLs that follow this template: cluster-identifier.account-number.aws-region.redshift.amazonaws.com.
8. Enter the Redshift schema and table (or view) that you want to use, then click Next. Learn how to format your data for Customer Match.
9. To map fields, select the data source fields from the dropdown lists that match the destination fields.
10. Optional: Apply transformations to your data.
11. Click Next.
12. Enter a name for this connection.
13. Optional: Edit connection details.
14. Click Finish.
15. Enter a name for the segment, confirm policy compliance, then click Save and continue.

Learn more about Customer Match.

Offline Conversion Import

1. In Google Ads, click Goals > Summary > + New conversion action.
   Note: If you already created a conversion and skipped adding a data source, click Tools > Data manager > Connected products > Connect source.
2. Click Import.
3. Select CRMs, files, or other data sources.
4. Select a tracking method.
5. Under Data source, select Connect a new data source.
   1. If you previously linked a data source from the Data manager screen that you want to use, select Select an existing source.
6. Search and select Redshift from the list of data sources.
7. Select Direct connection, then click Continue.
8. Read and accept the customer data policy, then click Continue.
9. Select a category from the conversion goal list, then click Set up.
10. Enter your Redshift database location, port, username, password, and database, then click Connect. By default, Redshift uses database location URLs that follow this template: cluster-identifier.account-number.aws-region.redshift.amazonaws.com.
11. Enter the Redshift schema and table (or view) that you want to use, then click Next.
12. To map fields, select the data source fields from the dropdown lists that match the destination fields.
13. Optional: Apply transformations to your data.
14. Click Next.
15. Enter a name for this connection.
16. Optional: Edit connection details.
17. Click Finish.
18. Click Save and continue.

Learn more about Offline conversion imports.

 

Apply a use case later

In this type of setup, you complete each part of the connection at different times. This can be useful when you are ready to link a data source, but you are not ready to set up your use case–for example, you aren’t ready to create a customer list.

Consider the following scenario: 

• Step 1: Dana is an engineer who manages data for your company. Dana sets up the data source to be used for activation in Google Ads. The data source is ready to be associated with a use case.
• Step 2: Mahan is a media specialist who needs to measure audience activation. Mahan creates a customer list and then associates it to the data source that Dana has previously set up, to use that data for Customer Match.

Step 1. Initiate the connection to the data source

Set up a data source and create a connection to be associated with a use case later.

1. In Google Ads, click Tools > Data manager > Connected products
2. Select + Connect product.
3. Under Data source, search and select a data source from the list of products.
4. If prompted, select Direct connection.
5. Select your use case, read and accept the customer data policy, then click Continue. If you select Audiences, you are prompted to select the data type at this step.
6. Enter your data source credentials*, then click Connect.
7. Enter the data location* that you want to use, then click Next.
8. To map fields, select the data source fields from the dropdown lists that match the destination fields.
9. Optional: Apply transformations to your data.
10. Click Next.
11. Enter a name for this connection.
12. Optional: Edit connection details.
13. Click Finish.

* Use the same credentials and location details for this data source as described in the first part of this article.

Step 2. Complete the connection by applying a use case

1. Open the destination you intend to use–for example, begin to create a new customer list or conversion action using the steps from the previous sections. Return to these steps before completing the data source step.
2. Under Data source, select Select an existing source.
3. Select the data source that was initiated in Step 1 from the list of data sources.
4. Continue to follow the remaining steps for your use case in the first part of this article. Note that you will skip further data source-related steps, such as entering credentials, because you completed these previously.

 

Troubleshoot errors

Setup errors

• Ensure that you are entering the correct database location URL and credentials.
• Ensure that the credentials you’re using have the appropriate permissions to access the table you need.

Scheduled run errors

• The most common cause of scheduled run failures is updated permissions in Redshift. Check that the role or account that corresponds to your account has permissions to read the data. 
• Ensure that the data still exists and that the names of the schema and table haven’t changed.