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With data virtualization for analytics at scale a central tenet, AtScale's Adaptive Analytics 2020.1 platform was unveiled on Wednesday.
The release marks a significant step for AtScale, which specializes in data engineering by serving as a conduit between BI tools and stored data. Not only is it a rebranding of the vendor's platform -- its most recent update was called AtScale 2019.2 and was rolled out in July 2019 -- but it also marks a leap in its capabilities.
Previously, as AtScale -- based in San Mateo, Calif., and founded in 2013 -- built up its capabilities its focus was on how to get big data to work for analytics, said co-founder and vice president of technology David Mariani. And while AtScale did that, it left the data where it was stored and queried one source at a time.
With AtScale's Adaptive Analytics 2020.1 -- available in general release immediately -- users can query multiple sources simultaneously and get their response almost instantaneously due to Augmented intelligence and machine learning capabilities. In addition, based on their query, their data will be autonomously engineered.
"This is not just an everyday release for us," Mariani said. "This one is different. With our arrival in the data virtualization space we're going to disrupt and show its true potential."
Dave Menninger, analyst at Ventana Research, said that Adaptive Analytics 2020.1 indeed marks a significant step for AtScale.
"This is a major upgrade to the AtScale architecture which introduces the autonomous data engineering capabilities," he said. "[CTO] Matt Baird and team have completely re-engineered the product to incorporate data virtualization and machine learning to make it easier and faster to combine and analyze data at scale. In some ways you could say they've lived up to their name now."
David MarianiCo-founder and vice president of technology, AtScale
AtScale has also completely re-engineered its platform, abandoning its roots in Hadoop, to serve both customers who store their data in the cloud and those who keep their data on premises.
"It's not really about where the AtScale technology runs," Menninger said. "Rather, they make it easy to work with cloud-based data sources as well as on premises data sources. This is a big change from their Hadoop-based, on-premises roots."
AtScale's Adaptive Analytics 2020.1 includes three main features: Multi-Source Intelligent Data Model, Self-Optimizing Query Acceleration Structures and Virtual Cube Catalog.
Multi-Source Intelligent Data Model is a tool that enables users to create logical data models through an intuitive process. It simplifies data modeling by rapidly assembling the data needed for queries, and then maintains its acceleration structures even as workloads increase.
Self-Optimizing Query Acceleration Structures, meanwhile, allow users to add information to their queries without having to re-aggregate the data over and over.
And Virtual Cube Catalog is a means of speeding up discoverability with lineage and metadata search capabilities that integrate natively into existing data catalogs. This enables business users and data scientists to locate needed information for whatever their needs may be, according to AtScale.
"The self-optimizing query acceleration provides a key part of the autonomous capabilities," Menninger said. "Performance tuning big data queries can be difficult and time-consuming. However, it's the combination of the three capabilities which really makes AtScale stand out."
Other vendors are attempting to offer similar capabilities, but AtScale's Adaptive Analytics 2020.1 packages them in a unique way, he added.
"There are competitors offering data virtualization and competitors offering cube-based data models, but AtScale is unique in the way they combine these capabilities with the automated query acceleration," Menninger said.
Beyond offering a platform that enables data virtualization at scale, speed and efficiency are other key tenets of AtScale's update, Mariani said. "Data virtualization can now be used to improve complexity and cost," Mariani said.