Sync data from
Amazon Redshift to Google Ads

You have a few options on how to sync your data models, product usage data, event data, and audiences to Google Ads. Some options are more manual than others so we've outlined the 5 most common routes people take below:

Option 1: Reverse ETL (Hightouch)

With Reverse ETL solutions like Hightouch, you can easily sync data directly from your warehouse to Google Ads and any of your SaaS applications. Hightouch never stores your data either, so you don’t have to worry about any compliance issues. All you have to do is define your data in Redshift using SQL or your own custom data models and map the appropriate columns to Google Ads.

On top of this, your marketing team can even build custom audiences directly in the Hightouch UI using a simple drag and drop interface leveraging the data models your team has built within Redshift. Getting started is relatively simple and all you have to do is follow these steps:

Step 1: Connect to Your Data Source

Step 2: Connect Hightouch to Your Destination

Step 3: Create a Data Model or Leverage an Existing One

You can use SQL to define your data directly in the Hightouch UI or even leverage your existing data models.

Step 4: Choose Your Primary Key

With Hightouch you can map on any unique key, not just users and accounts.

Step 5: Create Your Sync

Once you have created your sync, you simply have to choose the appropriate columns and map them to the fields in your end destination.

Step 6: Schedule Your Sync

Once your sync is created you can run it manually, define a set interval, or schedule it to run after a dbt job is completed.

The first integration with Hightouch is completely free so you can actually get started today. If you want to learn more about Reverse ETL, download our guide.

Option 2: Manual CSV Files

An easy way to move data from Redshift is simply by downloading specific a data set in the form of a CSV file and then uploading that file directly to Google Ads. This way of moving data creates several challenges though. Firstly, data stored in a CSV file becomes stale relatively quickly. In reality, Marketers want to test different audiences within Google Ads at a moment's notice.

This means that downloading specific data sets will not be a one-time ordeal. Every time your marketing team wants to test a new audience or campaign in Google Ads, you will be forced to download a specific data set. In an ideal world, you want your marketing team to be able to grab the customer data they need without ever having to go through your data team.

Option 3: Custom Integrations

Building a custom data pipeline from Redshift directly to Google Ads is one of the most efficient ways to move data into the platform. However, in-house data pipelines are extremely time-consuming to build and require heavy engineering resources. The APIs for Redshift and Google Ads are also constantly changing, which means you have to be on the lookout for upstream or downstream changes.

Depending on the amount of data that you are syncing to Google Ads, this could work for you, but scaling up this approach in the future is nearly impossible and building/maintaining more pipelines for additional Ad platforms like Twitter, Tiktok, etc., is nearly impossible.

Option 4: iPaaS (Integration Platform as a Service)

Point-to-point tools like Workato and Tray make it really easy to send data from one tool to another by enabling you to build custom workflows. However, these tools are better suited for automating tasks and processes between different SaaS applications. If your use case is simple, a tool like this could be a great option for moving data from Redshift to Google Ads because there is a relatively low barrier to entry.

However, if your needs are at all complex, you will find yourself writing custom code, and creating numerous if/then statements with tons of different dependencies in each intricate step of your workflow. Maintaining a workflow for all of your target destinations can be equally as challenging as building your own data pipeline in-house.

Option 5: CDPs (Customer Data Platform)

One of the most well-known options for moving data are CDPs. CDPs let you consolidate all of your customer data into a centralized platform where it can then be sent directly to Google Ads and other SaaS applications. The problem with CDPs is that they create another source of truth and they take your data out of your cloud environment, meaning that you no longer own it.

This can sometimes lead to compliance problems. CDPs can be a good option for some companies, but it's important to note that the average implementation time for a CDP is anywhere from six months to a year, so it can take a substantial amount of time to see any tangible results from your investment.