Google LLC today introduced speed-boosting capabilities for its BigQuery cloud data warehouse that it says will enable enterprises to run analytics workflows four to five times faster in some cases. 

BigQuery is a service in Google Cloud that companies use to analyze data from their operations to answer business questions. It lends itself to tasks such as inferring customer buying trends and predicting the number of packages that will go through an e-commerce fulfillment center in a future quarter.

The first enhancement announced today is the ability to create so-called materialized views. In database terminology, a materialized view is a cached copy of frequently used records. Those records can be, for example, inventory information that store managers regularly access as part of their work, or certain expense data often incorporated into reports from the accounting department.

Turning such information into a materialized view speeds up queries and thereby reduces wait times for users. When BigQuery needs to fetch a frequently used dataset, it can quickly load the ready-to-use materialized view for that dataset instead of sifting through a company’s entire information repository to find the needed items. 

The speed-up for users is especially noticeable when it comes to computationally intensive queries. Certain queries not only retrieve data but also perform processing operations, such as calculating the sum of all expenses listed in a database column. Using a ready-to-use copy of the results instead of calculating them from scratch not only saves time for users but can also cut companies’ cloud bills.

Google debuted the materialized view feature alongside upgrades to BigQuery’s BI Engine component. BI Engine is an in-memory analytics tool that lets users run data analyses and see the results in seconds. It facilitates such response times by storing the data being analyzed on RAM, which avoids the delay of shuttling it to and from storage.

A set of new integrations will let users harness BI Engine’s performance to speed up analyses they run in Google’s Looker and Connected Sheets products. Looker is a business intelligence platform that visualizes data in interactive charts. Connected Sheets, in turn, is a tool that makes it possible to analyze data stored in BigQuery via the interface of Google’s popular Sheets spreadsheet editor.

Google will also enable enterprises to use BI Engine with external analytics tools. The search giant said today that the in-memory analytics system can be paired with popular products such as Microsoft Corp.’s Power BI and Salesforce.com Inc.’s Tableau platform, among others.

The decision to support third-party business intelligence platforms aligns with the “open cloud” strategy Google Cloud Chief Executive Thomas Kurian outlined last year. BigQuery plays a central role in that strategy. 

Last July, Google debuted BigQuery Omni, a version of the data warehouse that can analyze information stored not only in Google Cloud but also competing platforms such as Amazon Web Services. The new support for external analytics tools in BI Engine should enable the search giant to more effectively accommodate customers taking a multicloud approach with their analytics projects.  

Image: Google