How SQream Manages Your Massive Data Sets | SQream

How SQream Conquers Massive Data

SQream was built to harness the raw brute-force power and high throughput capabilities of the GPU, on-premise, on the cloud or hybrid. With MPP-on-chip capabilities, the fully relational SQream DB SQL database comes with automatic tuning, ultra fast performance, class-leading compression, and extreme scalability.

Ultra-fast Ingest
SQream DB can load up to 3TB per hour, per GPU, while automatically optimizing and compressing the data
Columnar Engine
This feature allows selective access to the required subset of columns, reducing disk scan and memory I/O when compared with standard row storage. It is well-suited for parallelized compute, like the GPU.
AI-assisted Compression
SQream DB has the industry's first AI-assisted auto-compression, which figures out the best compression scheme for your actual data. This feature is made possible by the power of the GPU. SQream automatically compresses all the data it ingests, at a 5:1 ratio.
Hyperpartitioning - Chunks and Extents
SQream DB automatically splits up the storage horizontally into manageable chunks, enabling efficient usage of the hardware resources and relatively small GRAM (GPU RAM) availability in GPUs. The clever use of spooling and caching help make the most of the limited GRAM.

Built for Data Analytics at Scale

SQream DB was designed for massive, dynamic workloads. We built our algorithms to handle the most challenging scenarios, and optimized them for the largest datasets, where typical database optimizations fail.

Learn More About GPU Databases for Data Analytics

Learn More About GPU Databases for Data Analytics

Take a look inside SQream to see how GPU databases can help your business grow, with immediate access to raw data.