Sync data from
Google BigQuery to Salesforce

There are 5 main options on how to move data from BigQuery to Salesforce. Some of these options are more efficient and more secure while some are more manual and less secure. We've outlined your options below:

Option 1: Reverse ETL (Hightouch)

Reverse ETL is the easiest way to sync your data in BigQuery to Salesforce. With Reverse ETL tools like Hightouch, all you have to do is define your data in BigQuery using SQL or your own existing data models and then map that data to the appropriate columns in Salesforce. In addition to this, Hightouch never stores any of your data, so you can own your data pipelines from end to end.

Better yet, your marketing teams can even build custom audiences directly in the Hightouch UI using a simple drag and drop interface. On top of this, your data teams don’t have to worry about fulfilling ad-hoc requests, managing workflows, or fixing broken data pipelines.

Reverse ETL enables Operational Analytics and shifts the focus from simply understanding your data, to putting it to work in the tools that run business processes. Reverse ETL only takes a few minutes to implement and you can get started by following the steps below.

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

Downloading a specific BigQuery dataset as a CSV file and uploading that data directly to Salesforce is probably one of the easiest ways to move data, but it's not efficient. Data stored in this format gets stale really quickly and business teams can never truly rely on it. In an ideal world, you need an automatic daily flow of data from BigQuery to Salesforce.

When you are moving CSVs it requires your data team to fulfill ad-hoc data retrievals every time your sales or marketing team has a request. This is no minimal effort and time begins to add up pretty quickly when you do this over and over again on a daily basis. In addition to this, you also have to ensure that the columns in your file match those that exist in Salesforce, otherwise, you risk ruining all of your existing data because there is no undo button.

Option 2: Custom Integrations

Depending on the scale of your company, you could build your own data pipeline to move data from BigQuery to Salesforce. There are a couple of challenges with this approach. Firstly, building in-house is both time-consuming and resource-intensive and it pulls away from the time your engineers could be spending doing their actual jobs.

Secondly, the APIs for BigQuery and Salesforce are constantly changing, meaning that you have to check for upstream or downstream changes to ensure your pipeline continues working. Depending on your needs, building in-house might make sense, but if you are planning on sending data to additional SaaS applications in the future, it's simply not scalable

Option 3: iPaaS (Integration Platform as a Service)

Workflow tools like Tray and Workato can be a good option for moving data back and forth between SaaS applications. These tools let you build intuitive workflows that trigger actions when specific events happen.

However, when you are moving data from your warehouse to a destination like Hubspot, the workflows within these tools quickly become complex because they are not designed to combine data in the same way that a warehouse is. You will find yourself having to write custom code in order to get your workflows to function, and you will quickly notice that you have more if/then statements and dependencies within each step than you can possibly count.

Option 4: CDPs (Customer Data Platform)

CDPs provide a number of built-in features, but they essentially help you consolidate all of your customer data into a centralized 3rd-party platform so that it can be sent to other SaaS tools like Salesforce. However, CDPs create another source of truth in addition to your warehouse.

They also take your data out of your own cloud environment which can create problems around compliance depending on the industry that you work in. Additionally, from a time-to-value standpoint, CDPs are probably the slowest option, with the average implementation time taking anywhere from six months to a year.