Using Preferred Tables in Google BigQuery

How Google updated the BI Engine

Not long ago Google announced that BigQuery BI Engine support for all BI and custom applications was available. Now they launched the Preferred Tables feature in BigQuery BI Engine.

The BigQuery BI Engine is a fast in-memory analytics service. You can analyze data stored in BigQuery with a response time of less than one second and with high parallelism. When you connect Data Studio to a BigQuery table managed through BI Engine, reports and exploratory data analysis can be accelerated. Data Studio users get up to 1 GB of free capacity in BI Engine [1]. In addition to its own solution, Data Studio, looker, Tableau or Qlik can also be used with it.

You can now use the BI Engine preferred tables feature to limit BI Engine acceleration to a specified set of tables, while queries to all other tables use regular BigQuery slots. With preferred tables you can e.g. accelerate only the tables and dashboards that you identify as important to your business or which with you have performance issues. This makes a lot of sense, I know from my own experience that often the biggest tables can be the most important ones, making them faster will make users happy, while other smaller tables load faster anyway and may not be used here, the bi engine might even be a waste of money.

All you have to do to create a preferred table, is simply edit the existing BI Engine configuration in the project. You will see an optional step of specifying the preferred tables, followed by a box to specify the tables you want to set as preferred (see above)[3].

The following last updates regarding Google BigQuery might also interesting for you:

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