BigQuery is pretty fast anyway so you d really need to value the extra speed
Lindsay Clark Mon 1 Mar 2021 // 18:33 UTC Share
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Google has introduced a caching layer in Big Query - its cloud data warehouse - designed to speed up responses to users as they explore and experiment with data.
Under the moniker BI Engine, the data layer is designed to bridge the gap between BI tools that are built for interactivity like Tableau, which extracts data from the data warehouse, and an in-memory database for greater interactivity and Google s Looker, which queries data directly.
“The cloud databases. like BigQuery weren t built to support interactive BI, they were built for scale event analytics and scale-type questions,” said Colin Zima, chief analytics officer with Looker, the cloud BI biz Google bought for $2.6bn in February 2020.
Google Cloud Announces BigQuery Features That Accelerate Performance and Increase Efficiency for all Business Intelligence Workloads
BI Engine and materialized views give organizations performance gains across any business intelligence tool, from Google s Looker and Connected Sheets to Tableau, to Microsoft Power BI, and more
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SUNNYVALE, Calif., Feb. 25, 2021 /PRNewswire/ Google Cloud today announced two powerful enhancements that will continue to meet the customer demand of reducing time to insight and increasing performance of BigQuery. BI Engine and materialized views for BigQuery build upon the open, intelligent and flexible Google Cloud platform to provide sub-second query response time and high concurrency. The performance gains provided through these enhancements allows organizations to provide employees with increased access to data at scale and empower them with the ability to make fast data-driven decisions tha