Question

What is the difference between Looker and data studio?

Answer 1

The following parameters help you to know the differences between Looker and Data Studio:

1. Platform Architecture: Along with dashboarding, Looker is a data aggregation tool. It was designed from the bottom up to incorporate a wide range of data sources and give users the ability to aggregate and transform data using LookML.
2. While some transformations can be done within Data Studio, you'll be better off doing them in another platform like BigQuery and then consuming them through DataStudio.
3. Permissions: Looker gives you complete control over user and group management, as well as explicit permissions.
4. Data Studio's inherent simplicity allows you to regulate who can modify and view a dashboard and utilize a data source.
5. Version Control: Looker connects with GitHub so that several users can collaborate on a data model or dashboard simultaneously, with the ability to govern merging updates and rolling back modifications.
6. Although Data Studio wins for ease of use in version control, Looker's flexibility and depth of information are well worth the productivity cost.
7. Data Models and Blending: You can use Data Studio to connect to data sources and create a standalone model. Looker gives you more freedom when combining data sources, transforming data, and creating reusable reporting models. 
8. To obtain the same result in Data Studio, all work must be completed first on the underlying data platform. While Data Studio allows for considerable data blending flexibility, the underlying join is a left outer join, frequently the source of inconsistent reporting.
9. Data Caching: Data Studio allows you some control over your data cache by enabling you to decide whether it should query for new data in 15-minute intervals up to 12 hours at the data source level.
10. Looker gives you a lot more versatility in fine-tuning refreshes, which means less frustration for users waiting for their reports.