Snowflake Computing has announced the availability of new cloud data warehouse performance features to optimize query performance. When combined, automatic clustering and materialized views deliver increased speed for insight generation by all an organization’s Snowflake users.
Snowflake has a unique data warehouse as a service offering, and its own SQL data architecture that claims to do more than the alternative. The company has made a slew of improvements to its product portfolio recently, including Snowpipe, an automated data loading tool, a cloud data warehouse for the financial services, and a new Sharehouse add-on.
Automatic clustering optimizes the self-organization of data storage. It also merges and drops data to close gaps, which Snowflake does automatically and in the background. Non-blocking execution of ETL pipelines that contain DML statements ad incremental clustering to tables as new data arrives round out automatic clustering.
Materialized views offer performance improvement for queries that repeatedly use the same subquery results. Snowflake automatically maintains materialized views when new data arrives or existing data in the base table is modified. The tool will remain “always up-to-date” independent of modifications in the base table.
In a statement to the media, the company’s Vice President of Product Christian Kleinerman said: “Automatic clustering is the obvious next step for Snowflake, and similar to how we’ve automated security, maintenance and instant elasticity into our data warehouse-as-a-service. Materialized views in Snowflake take a fresh approach to an age old problem. We’ve leveraged the decades of experience we have with large-scale database processing, combined with the benefits of the cloud.”
This announcement comes on the heels of the company’s September news that its cloud data warehouse would be available on Microsoft Azure. Snowflake will utilize the Azure infrastructure for data storage and query processing while also integrating with services such as Azure Data Lake Store and Power BI.
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