Redshift

Overview

Hightouch lets you pull data stored in your Amazon Redshift data warehouse and push it to downstream destinations. Most of the setup occurs in the Hightouch UI, but you need access to Redshift for information like your host, port, database name, and credentials.

Connection configuration

To get started, go to the Sources overview page and click the Add source button. Select Amazon Redshift and follow the steps below.

Choose connection type

Hightouch can connect directly to Redshift over the public internet or via an SSH tunnel. Since data is encrypted in transit via TLS, a direct connection is suitable for most use cases. You may need to set up a tunnel if your Redshift instance is on a private network or virtual private cloud (VPC).

Hightouch supports both standard and reverse SSH tunnels. To learn more about SSH tunneling, refer to Hightouch's tunneling documentation.

Configure your source

Enter the following required fields into Hightouch:

• Host: The hostname or IP address of your Redshift cluster. The hostname can be found by visiting the Redshift web console, navigating to the Clusters panel, and clicking your cluster. Copy the Endpoint string, excluding the port and database name.
• Port: The port number of your Redshift cluster. The default is 5439, but yours may be different. To confirm, visit the Redshift web console, navigate to the Clusters panel, and click your cluster. The port number is shown in the Properties tab.
• Database: The name of the database in your Redshift cluster. Most clusters have only one database. Visit the Redshift web console, navigate to the Clusters panel, and click your cluster. The database name is shown in the Properties tab.

Choose your sync engine

For optimal performance, Hightouch tracks incremental changes in your data model—such as added, changed, or removed rows—and only syncs those records. You can choose between two different sync engines for this work.

The Basic engine requires read-only access to Redshift. Hightouch executes a query in your database, reads all query results, and then determines incremental changes using Hightouch's infrastructure. This engine is easier to set up since it requires read—not write—access to Redshift.

The Lightning engine requires read and write access to Redshift. The engine stores previously synced data in a separate schema in Redshift managed by Hightouch. In other words, the engine uses Redshift to track incremental changes to your data rather than performing these calculations in Hightouch. Therefore, these computations are completed more quickly.

Hightouch recommends using the Lightning sync engine when syncing more than 100 thousand rows of data.

If you select the Basic engine, you can switch to the Lightning engine later. Once you've configured the Lightning engine, you can't move back to the Basic engine without recreating Redshift as a source.

To learn more, including migration steps and tips, check out the Lightning sync engine docs.

Basic versus Lightning engine comparison

The Lightning sync engine requires granting write access to your data warehouse, which makes its setup more involved than the Basic sync engine. However, it is more performant and reliable than the Basic engine. This makes it the ideal choice to guarantee faster syncs, especially with large data models. It also supports more features, such as Warehouse Sync Logs, Match Booster, and Identity Resolution.

Lightning engine setup

To set up the Lightning engine, you need to grant Hightouch write access to Redshift. You can do so by running the following SQL snippet.

CREATE USER hightouch_user WITH PASSWORD '********';
CREATE SCHEMA IF NOT EXISTS hightouch_audit;
CREATE SCHEMA IF NOT EXISTS hightouch_planner;
GRANT CREATE, USAGE ON SCHEMA hightouch_audit TO hightouch_user;
GRANT CREATE, USAGE ON SCHEMA hightouch_planner TO hightouch_user;

The snippet creates a dedicated Amazon Redshift user for Hightouch. It also provisions two schemas, hightouch_planner and hightouch_audit, for storing logs of previously synced data.

Provide credentials

Enter the following fields into Hightouch:

• User: This can be your personal Redshift login or a dedicated user for Hightouch. At minimum, this user must have read access to the data you wish to sync. If using the Lightning sync engine, you must also grant this user additional permissions as described above.
• (Optional) Password: The password for the user specified above.

Test your connection

Next steps

Once your source configuration has passed the necessary validation, your source setup is complete. Next, you can set up models to define which data you want to pull from Amazon Redshift.

The Amazon Redshift source supports these modeling methods:

• writing a query in the SQL editor
• using the visual table selector
• leveraging existing dbt models
• leveraging existing Looker Looks
• leveraging existing Sigma workbooks

Tips and troubleshooting

If you encounter an error or question not listed below and need assistance, don't hesitate to reach out. We're here to help.

Spectrum nested query error

You may receive a Spectrum nested query error if you are using Amazon Redshift Spectrum as your data source. Hightouch uses a wrapper query around your model query for obtaining the COUNT of query results. Spectrum has limitations around nested data that this can causes issues with.

To resolve the error, you can try the following:

• Create a materialized view of the results set you want Hightouch to query.
• Add a commented out -- ORDER BY statement to your model definition. Hightouch's Redshift source disables the row counter wrapper query if there's an ORDER BY in the query. However, Redshift Spectrum doesn't allow ORDER BY statements, hence the need to comment it out. Even commented out, Hightouch disables the wrapper query.

Error: could not identify an ordering operator for type "unknown"

You may receive a this error if you are using many UNION set operators within your SQL query.

To resolve the error, you can try replacing UNION with UNION ALL. To learn more about UNION and UNION ALL, check out the article by The Data School.