How AtScale Uses Aggregates to Optimize Query Performance

The use of data aggregations (i.e. aggregates) to accelerate query performance is a common practice for data engineering teams, but the question remains how to balance resources like time and compute consumption in the aggregation process. Rather than relying on manual data engineering, AtScale accelerates analytics query performance using a novel combination of autonomous aggregate management and graph-based query optimization.

This approach allows the AtScale platform to harness the power of modern cloud data platforms (e.g. Snowflake, Google BigQuery, Amazon Redshift, Azure Synapse, Databricks) to improve the performance of business intelligence tools (e.g. Tableau, Power BI, Looker, Excel) while minimizing overall compute consumption.

To better handle aggregate management, AtScale leverages four approaches that incorporate both autonomous and human assisted. approaches.Below, this post will explore the basics of AtScale’s aggregate management with insight into  how this capability works within the AtScale platform to optimize query performance.

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