Google Cloud: Optimizing Performance of LookML Queries

Google Cloud: Optimizing Performance of LookML Queries

Category:Tutorial

US$9.99

Persistent Derived Tables Persistent derived tables are a great way to improve the performance of your Looker queries. By creating a persistent derived table, you can store the results of a query in a temporary table that can be reused by other queries. This can save you from having to run the same query multiple times, which can slow down your performance.

Aggregate Awareness Aggregate awareness is another important way to improve the performance of your Looker queries. By using aggregate awareness, Looker can take advantage of the fact that some of your data is already aggregated. This can save you from having to aggregate the data yourself, which can be a time-consuming process.

Performantly Joining Views Joining views can be a great way to improve the performance of your Looker queries. By joining views, you can combine data from multiple tables into a single table. This can make it easier to query the data and can also improve performance.

Using Indexes Indexes can be a great way to improve the performance of your Looker queries. By creating an index on a column, you can make it easier for Looker to find the data that you are looking for. This can save you from having to scan the entire table, which can be a time-consuming process.

Using Filters Filters can be a great way to improve the performance of your Looker queries. By using filters, you can limit the amount of data that is returned by a query. This can save you from having to process unnecessary data, which can slow down your performance.

Using the Right Data Type Using the right data type can be a great way to improve the performance of your Looker queries. By using the right data type, you can make it easier for Looker to process the data. This can save you from having to convert the data, which can be a time-consuming process.

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