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Tel Aviv-based company, SQream — which offers a GPU-accelerated data warehouse to handle complex queries and enable rapid analytics at scale — has announced a partnership with data management and digital infrastructure solutions provider Hitachi Vantara.
Under the engagement, the analytics company will integrate its data acceleration platform with Hitachi Content Software for File — a highly parallel NVMe-based file system — and Hitachi Content Platform (HCP) object storage. The combined solution, as the companies explain, will enable enterprises to perform rapid analytics on the full scope of data within their systems.
“The technologies which have been integrated are the SQreamDB Analytics platform, a GPU-based data warehouse designed to handle massive data sets using ANSI-92 SQL-compliant syntax, together with the Hitachi Content Platform (HCP) which features exabyte-scale local storage, multiple industry-standard APIs with a scale of 4 to 80 nodes,” Benny Yehezkel, chief revenue officer at SQream, told VentureBeat. “The goal of this partnership is to provide customers with substantial analytics requirements (up to peta-scale) with the best cost-performance joint platform.”
Impact of SQream-Hitachi Vantara offering
The offering is already available to enterprise customers, providing them the ability to analyze much larger stores of data with faster results and at a lower price point.
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“Enterprise customers around the globe including telecom, manufacturing and financial institutions using the solution can expect to be able to detect anomalies faster in both production, networks and in fraudulent activity,” Yehezkel said. “The outcomes of which result in saved time and money, and increased efficiency.”
In one case, a global manufacturer looking to enable anomaly detection via artificial intelligence (AI) used the joint offering to ingest and continually analyze a multi-peta scale database composed of manufacturing machine sensor events, ingested to thousands of tables. This led to a significant improvement in overall equipment efficiency.
Data is growing
The partnership comes as the volume of data within enterprises continues to grow, creating challenges in terms of analyzing all available information for accurate business insights and decisions.
Currently, companies looking to perform extensive analysis of massive datasets have to go through long extract, transform, load (ETL) processes and queries that result in organizations receiving valuable insights too late and less accurate AI and ML models.
As per IDC, the global datasphere is expected to grow from 33 Zettabytes (ZB) in 2018 to 175 ZB by 2025.
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