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SQream-Hitachi Vantara synergy to accelerate peta-scale data analytics

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Tel Aviv-based company, SQream that provides a GPU-accelerated data warehouse to take care of complex queries and enable rapid analytics at scale has announced a partnership with data management and digital infrastructure solutions provider Hitachi Vantara.

Beneath the engagement, the analytics company will integrate its data acceleration platform with Hitachi Content Software for File an extremely parallel NVMe-based file system and Hitachi Content Platform (HCP) object storage. The combined solution, because the companies explain, will enable enterprises to execute rapid analytics on the entire scope of data of their systems.

The technologies which were integrated will be the SQreamDB Analytics platform, a GPU-based data warehouse made to handle massive data sets using ANSI-92 SQL-compliant syntax, alongside 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 purpose of this partnership would be to provide customers with substantial analytics requirements (around peta-scale) with the very best cost-performance joint platform.

Impact of SQream-Hitachi Vantara offering

The offering has already been open to enterprise customers, providing them the opportunity to analyze much bigger stores of data with faster results and at a lesser price.


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Enterprise customers around the world including telecom, manufacturing and finance institutions utilizing the solution can get in order to detect anomalies faster in both production, networks and in fraudulent activity, Yehezkel said. The outcome of which bring about saved money and time, and increased efficiency.

In a single case, a worldwide manufacturer seeking to enable anomaly detection via artificial intelligence (AI) used the joint offering to ingest and continually analyze a multi-peta scale database made up of manufacturing machine sensor events, ingested to a large number of tables. This resulted in a substantial improvement in overall equipment efficiency.

Data keeps growing

The partnership comes because the level of data within enterprises is growing, creating challenges with regards to analyzing all available information for accurate business insights and decisions.

Currently, companies seeking to perform extensive analysis of massive datasets need to proceed through long extract, transform, load (ETL) processes and queries that bring about organizations receiving valuable insights too late and less accurate AI and ML models.

According to IDC, the global datasphere is likely to grow from 33 Zettabytes (ZB) in 2018 to 175 ZB by 2025.

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