14.11. Quicker Data Freshness with Blending & BigQuery
In Looker Studio, data freshness plays a crucial role in updating your reports with new information. A common problem when working with Google Sheets is that the shortest data freshness you can set is 15 minutes, which might not be ideal for some use cases. In this lesson, we'll explore a workaround to reduce the data freshness computing interval using BigQuery and data blending.
Reducing Data Freshness
BigQuery data sources in Looker Studio can be set with a shorter data freshness interval of one minute. Ideally, you would want to write data to a Google Sheet and read this data from BigQuery. While this is possible, a more practical solution involves blending data sources for quicker data freshness.
Blending Data Sources
To blend data sources and achieve quicker data freshness, follow these steps:
- Create a dummy BigQuery connection that doesn't connect to any table.
- Set the data freshness in BigQuery to one minute.
- Blend your primary data source (Google Sheet) with the dummy BigQuery connection.
With this setup, the blended data source will have a data freshness interval of one minute, as required by the BigQuery connection.
Considerations and Limitations
Although blending data sources allows you to achieve faster data freshness, it isn't a real-time solution. Users must still wait for the one-minute interval between updates. While this may not be ideal for all situations, it can serve as a temporary solution for cases where the data must be saved and accessed later.
Looker Studio's data freshness feature provides a helpful mechanism for keeping your reports up-to-date with the latest information. While it isn't always possible to achieve real-time data freshness, blending data sources with shorter data freshness intervals can help you get closer to this ideal outcome. By carefully considering data freshness requirements, you can optimize your reports for better accuracy and user experience.