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DDN, SQream Partner Up

April 2, 2018

DataDirect Networks has started a strategic partnership with SQream to deliver real-time Big Data analytics solutions and to significantly accelerate business decision making. The combination of DDN’s SFA and IME NVMe SSD storage platforms with SQream DB’s GPU-based Big Data SQL database (SQream DB) enables enterprises to effortlessly ingest, compress, store and analyze massive amounts of data.

“The ability to accelerate data queries and quickly analyze massive amounts of information is driving competitive advantage and valuable business insights across the market,” said Joel Sehr, VP of Americas at SQream. “The complexities of big data and data science models, particularly in data-intensive fields such as life sciences, telecommunications, cybersecurity, financial services and retail, require purpose-built database applications, compute systems and storage platforms. We are excited to partner with DDN and bring the benefits of its unsurpassed expertise in large-scale, high-performance computing environments to our customers.”

“The rapid and real-time analysis of massive amounts of data in order to better serve customer needs and gain a competitive edge is critical for enterprises across all markets and industry segments,” said Kurt Kuckein, director of marketing, DDN. “DDN’s SFA and IME high-performance NVMe SSD storage platforms provide business acceleration of more than 1000% in AI and Deep Learning applications. Our partnership with SQream delivers these capabilities to all GPU-based customer use cases on premise or in the cloud.”

The DDN Storage and SQream DB solutions provide a significant performance boost to an organization’s GPU framework by ensuring both the database and the underlying storage infrastructure are optimized for at-scale processing. Other storage solutions can be extremely limited when it comes to the data-intensive, extremely parallel nature of the GPU workloads. DDN’s purpose-built SFA and IME NVMe SSD appliances eliminate performance bottlenecks and deliver very high speed and extremely low latency responses to queries against even the most massive datasets.

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