Delivers Intelligent Data Virtualization and Expands Autonomous Data Engineering Capabilities for Today’s Cloud Analytics Workloads
AtScale, the intelligent data virtualization provider for advanced analytics, announced an unprecedented leap in multi-cloud and hybrid cloud analytics, data platform flexibility and time-to-analysis with the launch of its Adaptive Analytics 2020.1 platform release. Redefining traditional data virtualization and delivering upon the promise of cloud transformation, AtScale 2020.1 provides secure, self-service analysis while reducing compute costs by 10x, query performance improvements of more than 12.5x and enhanced user concurrency by 61x (Source: AtScale Cloud Data Warehouse Benchmark Report). Delivering on the promise of a single enterprise view of all analytics data, AtScale’s enhanced autonomous data engineering alleviates the performance and scale challenges of traditional data federation, manual data engineering and reliance on query caches. Additional enhancements in AtScale 2020.1 include a virtual cube catalog for simplified management of data assets and granular policy control that integrates natively with existing enterprise data catalog offerings.
“AtScale 2020.1 is a major step toward achieving our long-term vision of delivering intelligent data virtualization to every enterprise,” said Christopher Lynch, Executive Chairman and CEO of AtScale. “This release enables enterprises to alleviate the scale and performance limitations associated with their legacy analytics platforms and seamlessly embrace agile, hybrid cloud and multi-cloud data platforms, ensuring organizations have the ability to make informed decisions based upon all of their data.”
With the Big Data analytics market expected to reach $274B by 2022 (IDC forecast), the Fortune 2000 rely on AtScale to deliver unparalleled analytics ROI while alleviating the challenges associated with on-premise, proprietary analytic databases and the rapidly evolving ecosystem of cloud data platforms. “Our research shows that nearly three quarters (73%) of organizations currently use or plan to include data virtualization in their big data implementations,” said David Menninger, SVP and Research Director, Ventana Research. “AtScale’s latest release makes it easier to achieve both scale and performance for critical big data analytics by automating the data engineering tasks across today’s hybrid cloud and multi-cloud data platforms.” To illustrate this, Rakuten Rewards leverages AtScale for seamless migration from traditional on-premise data warehouses to Snowflake without business disruption, decreased query response times by more than an order of magnitude and minimized cloud compute resource consumption.
The AtScale Adaptive Analytics 2020.1 platform release includes:
- Multi-Source Intelligent Data Model – Create logical data models via an intuitive user experience without copying or transforming existing data structures. AtScale’s autonomous data engineering further simplifies and accelerates the user experience by assembling the data needed for queries in a just-in-time fashion and then maintaining acceleration structures for subsequent workloads.
- Self-Optimizing Query Acceleration Structures – AtScale incorporated additional information into the creation and lifecycle of acceleration structures, including data locale and platform capabilities. AtScale alleviates the “lowest common denominator” approach to query planning that results in significant resources being wasted on manual data provisioning and movement. AtScale’s Autonomous Data Engineering automatically determines the necessary structures and their optimal location.
- Virtual Cube Catalog – AtScale’s new virtual cube catalog accelerates discoverability with comprehensive data lineage and metadata search capabilities that integrate natively into existing enterprise data catalogs. This new capability translates directly into business semantics and empowers business analysts and data scientists to locate the necessary data for business intelligence, reporting and AI/ML activities.
“We’ve researched extensively how AtScale can continue to automate data engineering, specifically trying to understand the key considerations of modern data engineering teams. In most cases, they don’t have perfect information or a complete view of the underlying data platform capabilities, with AtScale we have a unique view of both and can optimize for performance, security, agility and cost – in a completely autonomous way” – Matthew Baird, Co-Founder, and CTO, AtScale.