Snowflake Computing, a data warehouse built for the cloud, is introducing two new performance features – automatic clustering and materialized views – that will optimize query performance, eliminating the manual work associated with other data warehouse solutions.
When combined, these features can deliver an exponential increase in performance, generating even faster and deeper insights from all of an organization’s data by all of its business users.
Snowflake’s automatic clustering provides the following benefits:
- Automated and optimized self-organization of data storage, removing the burden of manually re-clustering data
- Merging and dropping data, and closing gaps between data, which Snowflake manages automatically and in the background
- Non-blocking execution of ETL pipelines that contain DML statements
- Incremental clustering to tables as new data sets arrive
- Exponentially faster queries with lower maintenance and lower cost
Snowflake’s materialized views provide the following benefits:
- Significant performance improvement for queries that repeatedly use the same subquery results
- Automatic maintenance of materialized views when new data arrives or existing data in the base table is modified
- Fast DML operations on base tables in the presence of materialized views
- Always up-to-date data when accessed via materialized views independent of modifications in the base table
“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,” said Christian Kleinerman, Snowflake’s vice president of product. “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, to provide materialized views that are transactionally consistent without slowing down modifications to the base tables.”
For more information about this update, visit www.snowflake.com.