SQream Platform
GPU Powered Data & Analytics Acceleration
Enterprise (Private Deployment) SQL on GPU for Large & Complex Queries
Public Cloud (GCP, AWS) GPU Powered Data Lakehouse
No Code Data Solution for Small & Medium Business
Scale your ML and AI with Production-Sized Models
A SQL database that empowers organizations to perform complex analytics on petabyte-scale of data and gain time-sensitive business insights, faster and at one tenth of the cost.
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.
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.
SQream vs Databricks on AWS benchmarks
SQream GPU-acceleration unlocks linear scalability for TPC-DS
SQream Tackles Telecom Network Planning On-Cloud
AIS Thailand turns billions of records of siloed data into better network management and a competitive advantage
Electronics giant improves yield from 50% to 90%
Cloud comms provider responds to 40,000+ customer queries a day and makes it look easy
How to: Create a table, load data, and run a query
How to: Create a Foreign Table in SQreamDB
How to: Use SQreamDB Acceleration Studio
GPU-accelerated AI and Data Processing
GPU-accelerated AI and Data Processing