Where can you add this connector in Funnel? | Which subscription plan do you need? |
| A Business or Enterprise plan |
In Funnel, you can connect your Google's BigQuery account as one of the data sources. Each data source and report type is a connected source.
Guidelines and requirements
Before you begin connecting, ensure that you meet the following requirements:
Ensure to have the required project roles and permissions in BigQuery.
Contact your project administrator if you don’t have access to BigQuery projects.
Understand the difference between BigQuery tables and Views. Refer to Google’s BigQuery documentation for more information.
Depending on if you partitioned your tables based on date, BigQuery computes costs accordingly. Read Optimize query computation and BigQuery pricing for more information.
Best practices for managing BigQuery costs
Always partition tables by date in BigQuery.
Avoid importing a view unless you understand the pricing structure.
Monitor query costs in the BigQuery console.
Preview the table in BigQuery to check estimated bytes.
Collaborate with your data team to restructure large datasets where needed
Read Optimize query computation and BigQuery pricing for more details.
Procedure
Follow these steps to connect to your Google’s BigQuery account as a data source in Funnel:
In Funnel, go to Connect > Data sources, and click + Connect Data Source.
Search for Google BigQuery in the search bar, and click Connect Now.
In the Select Credentials section, select your BigQuery account’s credentials so that Funnel can download your data.
If you select an account that you already added, go to step 4. If you are adding a new account credential, follow these substeps.
You have the following two options to add your account’s credentials:
Continue with Google: If you click Continue with Google, you will be redirected to a screen to confirm the Google account you want to continue with.
Connect a service account: If you click Connect a service account, enter your Google’s service account JSON and click Next.
Note: In this procedure, we are going to connect to a Google account.
Click Continue to confirm the account.
Review the access permissions for the Funnel Connector, and click Allow.
Select a project, and click Next.
You can select only one project per connection. Create another connection to add another project as a data source.
Select a dataset, and click Next.
You can select only one dataset per connection. Create another connection to add another dataset as a data source.
Select a table or a view, and click Next.
Note: You may see additional compute pricing costs in BigQuery if you select a view. Since a view is defined by a SQL query, it may contain more than one table and this may lead to more frequent data downloads than when you select a table.
Select the columns and fields you want to import in the Fields section.
(For data containing columns with a monetary unit) Select a currency for all your data or select a column that specifies a currency in one of its rows.
When you Select a field containing the currency, the columns with a string value are populated in the drop-down list.This section is greyed out if your data doesn’t have any columns with a monetary unit.
(For data containing columns with a date unit) Select how Funnel should fetch your BigQuery data and store.
You have the following options:Date by daily date: Select a column that has a date unit. Only columns with a date value are populated in the drop-down list.
Dateless: Select this option if you want Funnel to fetch data with no date mapping. If you import large tables, the costs may be high because BigQuery scans the entire table. Refer to the Optimize query computation section for best practices to optimize your query performance.
This section is greyed out if your data doesn’t have any columns with a date unit.
Important: The computing costs depend on the option you select. Read the Factors affecting BigQuery computing costs section to understand the cost expectations.
Define the values in the Download Configuration section.
Select values in the following fields:How often should Funnel download your data? Select one of the options from the drop-down list. You can choose to download data in hourly frequencies or every day.
(Available only if you selected Date by daily date in the previous step) How much historic data would you like? Select a date from when Funnel should start downloading the data.
(Available only if you selected Date by daily date in the previous step) How much historic data should be refreshed with each download? Enter the number of days for which Funnel should refresh the historical data. The days are usually counted backwards from the current day.
Assume today’s date is 28 May 2025 and you entered the following values for the following three fields:How often should Funnel download your data? Every day
How much historic data would you like? 1 Apr 2025
How much historic data should be refreshed with each download? 3
Funnel, then, refreshes the historical data only for today and the last two days, which is from 26 May 2025.
Important: The computing costs depend on the option you select. Read the Factors affecting BigQuery computing costs section to understand the cost expectations.
Click Next.
Review the configuration summary, and click Connect Data Source.
Factors affecting BigQuery computing costs
When you are importing data from a Google’s BigQuery data source to Funnel, you may see additional compute price based on your configuration settings. Some of the configurations that affect these costs are:
How you create a table or view in BigQuery
Options you select in the Date mapping section and the Download Configuration section in Funnel
Here are some of the different combinations of the above factors and how they may affect the costs:
Connection configurations in Funnel | Table partitioned by date in BigQuery? | What happens in BigQuery and in Funnel | BigQuery cost expectations |
| Yes |
| Query cost is optimized |
| No, but has a date column |
| May lead to higher costs |
| Mixed partitioning (if you select a view combining different types of tables) |
| May lead to higher costs |
| Not applicable |
| May lead to higher costs |