Speed-of-thought without the management headache through automated query optimization.
AtScale’s Autonomous Data Engineering™ identifies scenarios and applies multiple strategies the way a world-class data engineering team would respond. The AI-driven optimizer learns from user behavior and data relationships to improve data agility, security and performance. No more manual data requirements. Using automated data lineage metadata, leave data updates and changes to AtScale.
Eliminate scripting, tedious query performance tuning, and redundant summary tables. Avoid costly runaway queries that scan raw data, and vastly increase query throughput and concurrency.
Get continuously improved performance through a combination of query signals and statistics collection to deliver interactive response times without manual data engineering.
Data changes and use of data changes, constantly. Simply declare your business model in AtScale's Design Center and let your users analyze without limitation. AtScale will handle the rest.
Make the most of the data infrastructure that you've invested in. AtScale enables your use cases to scale analytics into production using data engineering best practices such as caching overhead management with columnar aggregate storage, smart partitioning, nested data types and pre-aggregation.
Analytics on analytics: one place to consume data, means one place to monitor and measure. AtScale's Design Center shows all queries in a single UI and IT-friendly API so you can see exactly how your query executes within your data infrastructure.
A Fortune 50 insurance provider ran analytics for all of its member data on a large Netezza environment. The insurance provider wanted to migrate off of Netezza due to cost reasons, and elected to move its data to a Hortonworks Hadoop cluster. However, when analysts queried data in the new Hortonworks cluster directly from Tableau and Excel, queries took minutes to return data, well above the sub-three second response time business users required. Additionally, connecting Tableau and Excel directly to Hortonworks only served a few hundred analysts, a small subset of the insurance provider’s total analyst constituency.
With A3 sitting between Tableau and Excel and Hortonworks, the insurance provider’s query response time accelerated by as much as 68X, and easily met the requirement for queries to be answered under three seconds. The improvement in query optimization enabled the insurance provider to open up the data in their Hortonworks cluster to as many as 4000 business users. The insurance provider is now able to expand analysis to encompass perscription data, and can deliver better client service through proactive insights.
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