My Step-By-Step Blueprint For Learning Google Analytics BigQuery

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Last Updated: August 22, 2022

Google Announced on March 16, 2022, that it will discontinue Universal Analytics (GA3) on July 1st, 2023. So if you decide not to upgrade to GA4 then your current Google Analytics tracking would automatically stop working after July 1, 2023. And to make matter worse, Google will also delete all of your historical universal analytics data in 2023.

If you want to save your historical GA3 (Universal Analytics) data from being deleted, then import it into BigQuery. 

By backfilling Google Analytics data in BigQuery, you can export historical data into your BigQuery project. 

Why backfill GA3 data in BigQuery?

Backfilling GA3 data in BigQuery means importing historical GA3 data into your BigQuery project.

When you send GA3 data to a BigQuery project via a connector like ‘Supermetrics’ you send only a couple of days of data to BigQuery.

If you want to send a large volume of historical GA3 data to BigQuery, you need to backfill GA3 data in your BigQuery project.

Prerequisites for backfilling Google Analytics data in BigQuery

Before you can backfill GA3 data in BigQuery:

#1 You would need a Google Cloud Platform account with billing enabled.

#2 You would need a BigQuery project with billing enabled where you are going to store the GA3 data.

#3 You would need a third-party solution (connector) for sending GA3 data to BigQuery without using Google Analytics 360.

#4 Your BigQuery project must be associated with at least one active data transfer service.

#5 Your initial data transfer must have been completed successfully for your chosen data source.

Note: Different data sources (like Google Analytics, Google Ads, Facebook etc) have different data retention policies, which could restrict the amount of data you are allowed to backfill.

If you are using a third-party solution/connector (like Supermetrics) to connect to a data source, then the amount of data you are allowed to backfill will depend upon the connector being used. 

For example,

‘Supermetrics for BigQuery’ connector allows you to backfill up to six months’ worth of data at one time.

If you want to backfill more data, then you would need to do it in separate batches of six months sized.

My Step-By-Step Blueprint For Learning Google Analytics BigQuery

Get the only ebook on Google Analytics BigQuery ever published (60+ Pages)

Yes I want the FREE ebook

Follow the steps below to backfill Google Analytics data in BigQuery:

Step-1: If you are not already sending Google Analytics data to BigQuery, then first follow the instructions in this article: How to send data from Google Analytics to BigQuery without using GA 360

Step-2: Navigate to your BigQuery account: https://console.cloud.google.com/bigquery

Step-3: Make sure that you are in the project whose data transfer service you want to edit for backfilling GA3 data:

Step-4: Click on the link ‘Data transfers‘ from the left-hand side navigation:

Step-5: Click on the name of the data transfer service you want to edit:

Step-6: Click on the ‘SCHEDULE BACKFILL‘ button to backfill Google Analytics data into your BigQuery data table:

You should now see a dialog box like the one below:

Step-7: Click on ‘Run for a date range‘:

Step-8: Select the start date and time and end date and time for the Google Analytics backfill and then click on the ‘OK’ button to start the data transfer:

You should now see the backfill scheduled notification at the bottom of your screen:

Step-9: Refresh your browser window.

For each day in the selected date range, a new data transfer service will be added to your run history:

Step-10: Wait for the data transfer to complete. This could take some time depending upon how much data you requested to be backfilled:

Step-11: Click on the ‘SQL workspace‘ link from the left-hand side navigation:

Step-12: Navigate to the dataset which contains the data table(s) that contain the backfilled Google Analytics data:

You should now be able to see the backfilled Google Analytics data for a particular day from the date drop-down menu:

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