Caching and Datagroups with LookML
Looker is a modern data platform in Google Cloud that lets you analyze and visualize your data interactively. You can use Looker to do in-depth data analysis, integrate insights across different data sources, build actionable data-driven workflows, and create custom data applications.
Looker is constantly generating SQL queries and sending them to the connected database. Whenever someone runs new a query in Looker, the SQL results are cached and stored in an encrypted file on the Looker instance.
Caching leverages the saved results from previously executed queries, so that the same query does not need to be run on the database each time. This helps to reduce database load.
A datagroup is the Looker term for a caching policy or rule. LookML developers use datagroups to manage caching on a Looker instance.
Caching is a useful feature that reduces database load and helps to optimize Looker performance. In this lab, you will learn how caching works in Looker and explore how to use LookML objects called datagroups to define caching policies.
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