Store and manage enterprise-scale data, so decision-makers, business analysts, data engineers, and data scientists can analyze the data and gain valuable insights from BI, SQL clients, and other analytics apps.
Transform raw data through denormalization, pre-aggregation, feature generation, cleaning, and BI processes, so it can be ready for Machine Learning and AI processes.
Analyze data from any source, in any technology, and in any format, on top of existing analytical solutions and without any data duplication required.
Business Intelligence and Visualization
ML & Data Science
Data Integration
Processing is performed by using the ANSI-SQL syntax. Running queries can be done through the built-in SQream Acceleration Studio, or through a third-party BI tool.
SQream integrates into existing ecosystems, with support for industry-standard ODBC and JDBC connectors, as well as Python and C# .Net, C++, Java, and others.
The GPU is used to achieve parallel data processing. By splitting large tasks into smaller processes, SQream distributes operations between multiple GPU cores.
All the data that is it ingested is automatically compressed at a 5:1 ratio
The compute and storage are completely separated, with multiple compute units, running to store or retrieve data from a single or multiple storage sources. This concept provides flexibility and easy scaling, while data processing is being done not in memory.
Acceleration leans on synchronizing all available resources (CPU, GPU, RAM) for complex analytical tasks while performing automatic vertical and horizontal partitioning of the data. moreover, it stores data tables by columns, therefore eliminating unnecessary reading for each analytical workload.
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