Sync data from
Snowflake to Google Ads

We've outlined the 5 easiest options you have to activate your Snowflake data and get it into Google Ads for targeting, suppression, and conversion tracking.

Option 1: Reverse ETL (Hightouch)

Reverse ETL tools like Hightouch run on top of Snowflake. This means you can sync data directly from Snowflake to Google Ads in real-time, without having to worry about any data pipelines, CSV files, or complicated workflows. All you have to do is define your data using SQL or use your existing models and map that information to the appropriate columns in Google Ads.

Hightouch does not store any data, so you don’t have to worry about any compliance issues. Instead, you can solely focus on syncing your data from your cloud data warehouse to Google Ads. Hightouch also provides an intuitive interface for your non-technical users so they can create custom audiences directly within the platform using the data models your team has already built-in Snowflake. Reverse ETL is surprisingly simple and only takes a few minutes to set up if you 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

The easiest way to move data from Snowflake to Google Ads is to simply download a manual CSV file. This poses two main challenges though. Firstly, it’s not a one-time data pull. Your engineering team has to download a new data set every time your marketing team wants to target a different audience or run a different experiment.

On top of this, data stored in a CSV file becomes stale and unusable really quickly. In almost every scenario your marketing team wants the real-time data or the closest thing to it, so they can iterate at a moment’s notice and make better business decisions. When your engineering team is forced to fulfill constant Adhoc requests it pulls away from the jobs they should actually be doing.

Option 3: Custom Integrations

In most cases moving data from Snowflake to Google Ads requires you to build a custom in-house data pipeline. The APIs for Snowflake and Google Ads are constantly changing though, so maintaining a homegrown data integration pipeline can be quite challenging, and even getting a modern data pipeline up and running takes a substantial amount of time and resources.

Depending on the size of your organization, this could be doable, but maintaining this at any sort of scale quickly becomes an impossible feat, especially if you need to build more pipelines to additional advertising platforms in the future (ex: Tiktok, Facebook, Twitter, etc.).

Option 4: iPaaS (Integration Platform as a Service)

Another way to move data from Snowflake to Google Ads is through the usage of iPaaS solutions. Tools like Workato and Tray provide you with a relatively simple platform where you can build custom workflows to move data from one tool to another.

The problem is, these tools are really designed to handle simple use cases and automate repetitive tasks, so if you go beyond simple use cases, you will be stuck building complicated workflows with numerous if/then branches, all with various dependencies within each step.

Option 5: CDPs (Customer Data Platform)

CDPs do a decent job at moving your data from point “A” to “B” because they are marketing platforms that consolidate all of your customer data into a centralized platform, where that same data can be ingested in various other tools like Google Ads. There is a problem though, CDPs don’t necessarily play nicely with your existing technology stack and create another source of truth.

Additionally, CDPs own your data since it is no longer in your own cloud environment. Figuring out how to move your data into a CDP can be equally as challenging as building an in-house data pipeline. In addition to this, the typical implementation time for a CDP can take well over a year.