The increasing value of big data analytics for business presents a lot of use cases for BigQuery technology. Through Google Analytics to BigQuery automation, data analysts can save time as well as extract deeper insights from the latest Google Analytics data.

1. Implementing Google Big
Query Automation Using
Google Analytics Data
www.countants.com

2. Overview
• What is Google Analytics to BigQuery Automation?
• How to Configure the Google Analytics to BigQuery
Automation Process
• Business Benefits of Google Analytics to BigQuery
Automation
• Conclusion

3. What is Google Analytics to Big
Query Automation?

4. Google Analytics to BigQuery
Automation process is an automated
BigQuery job that eliminates any
manual querying tasks. This typically
includes tasks like importing Google
Analytics data to BigQuery or
executing any SQL queries.

5. How to Configure the Google Analytics
to BigQuery Automation Process

6. 1. Google Cloud Storage
BigQuery supports the data loading from Cloud Storage in various formats including
Avro, CSV (default), JSON, and ORC. You can use the Google console directly to
perform this loading.

7. 2. POST request
Another option is to post the data directly using JSON APIs. Google Analytics API play a
crucial role in both the loading and extraction of data into the BQ data warehouse. For
instance, you can execute the HTTP POST request with the CURL or Postman tool.

8. i. Create
your query in
the editor
tool

9. ii. Create the
new schedule
query using the
“Schedule
query”

10. iii. The next step is to schedule the
new query using the “New Scheduled
Query” option. Apart from the
scheduled query name, ensure that
you specify the target table name.
Besides these, you can specify the
following configuration settings:
• Repeat frequency: when the
queries must be repeated (daily,
weekly, or monthly) along with the
start and end date & time
• Table write preference: where you
can specify if the query should
either overwrite or append data to
the target table.
• Email notifications: where you can
select if you wish to be notified on
email of the execution of a
successful query.

11. iv. Finally, click “Schedule” to schedule your query with the above
settings.

12. Business Benefits of Google
Analytics to BigQuery Automation

13. 1. Advanced
Cohort
Analysis

14. • Using Google Analytics,
businesses can use Cohort
Analysis to estimate
customer retention based on
their acquisition date.
• Big Query automation can
extend the capabilities of
cohort analysis using Google
Data Studio. BQ jobs can be
automatically executed to
process the GA data, store
them in staging tables, and
finally populate the cohort
analysis report each month
with new and updated data.

15. 2. Data Pre-Processing and Cleaning
• Automated BigQuery jobs can be useful when it comes to pre-
processing and cleaning of Google Analytics data.
• For instance, pre-processed data in Google Analytics shows multiple
page-wise metrics such as page views, average page time, and
bounce rates
• Additionally, BigQuery automation can be used to clean the GA data.

16. 3. Integration with other data sources
• BigQuery automation allows you to integrate your Google Analytics
data with other external data sources. As an example, traditional
offline data such as transactions in a brick-and-mortar store can be
integrated seamlessly with BigQuery.
• Another example of seamless integration is the ability to move BQ
data to Google Cloud Storage using Google Apps Script any cloud-
based tool.

17. 4. Data Science
• Finally, BigQuery job automation is useful for data scientists and analysts for
updating their data models based on new data. For instance, BQ jobs can be
scheduled to create a pipeline that can automatically add new users to a
specific user cluster based on their online behavior.
• This type of automated user assignment is often used by digital marketers
looking to identify and group users based on common interests.

18. Conclusion
The increasing value of big data analytics for business presents a lot of
use cases for BigQuery technology. Through Google Analytics to
BigQuery automation, data analysts can save time as well as extract
deeper insights from the latest Google Analytics data.