16.7. Google Sheet vs BigQuery Data Pipeline
In this lesson, we'll compare the Google Sheet-based data pipeline with the BigQuery data pipeline. We'll discuss their differences and explore why you might want to switch from one to the other in your Looker Studio projects.
Traditional Google Sheet-Based Data Pipeline
The traditional method of creating a data pipeline involves using multiple Google Sheets. Let's say you're working with Facebook data, Google Analytics data, and Google Ads data. You might use a connector like Supermetrics or the GA add-on for Sheets to import this information into separate sheets.
Once you have all of your data in place, you would perform calculations, join tables, and manipulate the information within these sheets to create a master sheet. Finally, you'd connect this master sheet to Looker Studio for visualization and sharing with clients.
The BigQuery Data Pipeline
The concept behind the BigQuery data pipeline is similar but offers some key advantages in terms of efficiency and scalability.
Instead of importing your Facebook, Google Analytics, and Google Ads data into separate sheets, you would import them into different BigQuery tables. From there, you'd perform your modeling and calculations within BigQuery itself.
After all necessary transformations are complete, you would create a final table or view in BigQuery which can then be connected directly to Looker Studio for visualization and sharing purposes.
Why Switch from Sheets to BigQuery?
While both methods achieve similar results, BigQuery offers some distinct benefits over using multiple sheets:
- Improved performance: As your dataset grows larger or more complex over time, BigQuery can handle these increases in scale more efficiently than Sheets.
- Enhanced collaboration: Multiple team members can work on the same dataset simultaneously without causing conflicts.
- More powerful analysis: With advanced SQL capabilities at your disposal within BigQuery itself (and integration with other tools), it becomes easier to perform complex analyses with more accuracy and speed.
By switching to a BigQuery data pipeline, you'll be able to harness these benefits for more efficient, powerful, and collaborative Looker Studio projects.