AtScale, which provides a universal semantic platform for BI on big data, has announced general availability of AtScale 6.0 which, the company says, reduces the cost of queries on Google Big Query.
With this release, AtScale says it is bringing its capabilities to Google BigQuery, allowing enterprises to experience faster time to value on any data, regardless if it’s stored on premises, in the cloud, in Hadoop or in a relational database.
With the AtScale Adaptive Cache, a technology that continually analyzes query patterns and automatically creates and manages aggregates, users get sub-second query results and put significantly less load on BigQuery, resulting in savings. AtScale’s Adaptive Cache graph engine optimizes the processing of aggregates, starting with the finest grain aggregates first before moving to more summarized aggregates. According to AtScale, early deployments have shown aggregate processing time to improve by up to 10X, the company says.
More importantly, the company says, by re-routing big data queries to its Adaptive Cache, AtScale 6.0 reduces the number of unique requests hitting the underlying infrastructure. This helps with reducing infrastructuring” stress and it also lowers the cost incurred for each query. In initial testing on Google BigQuery, AtScale says, query costs have been reduced by up to 1,000X per query.
For more details, go to www.atscale.com/try.