Implementing Google Big Query Automation Using Google Analytics Data
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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
What is Google Analytics to Big Query Automation?
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.
How to Configure the Google Analytics to BigQuery Automation Process
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.
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.
i. Create your query in the editor tool
ii. Create the new schedule query using the “Schedule query”
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.
iv. Finally, click “Schedule� to schedule your query with the above settings.
Business Benefits of Google Analytics to BigQuery Automation
1. Advanced Cohort Analysis
• 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.
2. Data Pre-Processing and Cleaning
• Automated BigQuery jobs can be useful when it comes to preprocessing 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.
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 cloudbased tool.
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.
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.