Sync data from
Google BigQuery to Google Ads

There are 5 common options to activate your BigQuery data and sync it with Google Ads. We've outlined and reviewed the pros and cons of each option below:

Option 1: Reverse ETL (Hightouch)

Reverse ETL leverages your own technology stack and runs on top of the warehouse. This means that you own all of your data and you don’t have to worry about another 3rd party vendor. Using tools like Hightouch you can define your data in BigQuery with your own custom data models or SQL.

After this, all you have to do is map the appropriate columns from Google BigQuery to Google Ads and start your data sync. You can schedule this manually or run your syncs on a set interval. All of this can be done in six simple 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

A relatively simple way to move data to Google Ads involves downloading CSV files from BigQuery and uploading them directly to Google Ads. The problem with this method is that data goes stale really quickly when it is stored in a CSV file.

This way of moving data is also extremely manual. Your data engineers are forced to download/upload a different data set every time your marketing team wants to run a new campaign or target a different audience. Doing this pulls away from the actual job they could be fulfilling.

Option 3: Custom Integrations

Building your own in-house data pipeline is one way to tackle the challenge of moving data from Google BigQuery to Google Ads. This can be extremely challenging though because custom data pipelines are time-consuming to build and difficult to maintain. The APIs for Google BigQuery and Google Ads are constantly changing, which means you have to constantly update your data pipeline upstream or downstream depending on where this update takes place.

Depending on your scale, it's possible that a custom data pipeline could suit your needs, but if you are planning on sending data to additional Ad platforms in the future, this is not scalable. Your engineers will be building and maintaining data pipelines instead of doing the jobs they were actually hired for.

Option 4: iPaaS (Integration Platform as a Service)

Integration platforms are relatively simple to implement because they let you build intuitive workflows to push data from one system to another. In general, these tools are mainly used for automating different tasks and processes to improve efficiency. They move your data from point “A” to “B” or the inverse of that. The problem is, these workflows create an illusion of accessibility.

At their core, they perform an event when a trigger is met and if you try to do anything remotely complex or unique to address your business needs, you will find that these workflows soon become extremely difficult to manage, with multiple if/then statements and various dependencies in every step. In many cases, you will even need to write some custom code to get them to work in the way that you want.

Option 5: CDPs (Customer Data Platform)

With a CDP you can easily consolidate all of your customer data into a central platform where it can then be sent directly to Google Ads. This is slightly problematic though because it creates a second source of truth in addition to your data warehouse.

Using a CDP requires you to move data out of your own cloud ecosystem and into a 3rd party vendor and this can create challenges from a compliance standpoint. Although CDPs are a great solution for marketers, they can be quite challenging to implement, with the average time taking anywhere from six months to an entire year. From a time-to-value standpoint, this is not very effective.