Enables Even Faster Ingest and Analysis of Massive Amounts of Data for Accelerated Analytics and Previously Unattainable Insights
SQream, developer of SQream DB, the GPU-accelerated data warehouse designed for rapidly analyzing massive data stores at a fraction of the cost, announced support for NVIDIA DGX-2 at the GPU Technology Conference.
SQream DB enables enterprises to quickly and easily load massive volumes of data in the range of terabytes to petabytes for analysis, while generating higher quality business intelligence faster than any other data store at these volumes. SQream DB already supports NVIDIA DGX-1, and the added support for NVIDIA DGX-2 will allow customers to load and analyze very large data stores even faster.
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SQream representatives will be at the San Jose McEnery Convention Center, March 19-21, at booth #526 to offer insights on how enterprises can achieve their big data analytics objectives using SQream DB with GPU technology.
“SQream’s support for NVIDIA DGX-2 will provide our customers with the ability to realize even more rapid business intelligence from very large data stores,” said SQream co-founder and CEO Ami Gal. “This will build on the success we already have with enterprises in telecom, finance, retail and healthcare who are achieving significant results from insights that are providing new revenue growth paths as well as cost savings and efficiencies.”
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SQream DB customers will be able to build on NVIDIA DGX-2 high-throughput analytical pipelines from ingest to query for historical data analysis, trend identification, fraud detection, outlier detection and more. Combining SQream DB with other GPU-accelerated machine learning and artificial intelligence frameworks such as TensorFlow and H2O, allows users to create extensive proactive analytics pipelines on very large amounts of data.
NVIDIA DGX-2 is the first 2 petaFLOPS system that combines 16 fully interconnected GPUs for 10X the performance of an 8-GPU system. It is powered by NVIDIA DGX software that enables accelerated deployment and simplified operations at scale, as well as a scalable architecture built on NVIDIA NVSwitch.
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