AI TOOL PROFILE
SQream: GPU-Powered Data & Analytics Platform
- Data and Analytics
- Analytics and Reporting
- Enterprise companies
- Mid-market companies
- Data engineering teams
- AI and ML practitioners
Pricing
SQream uses an annual subscription model per GPU. Pricing was not clearly available from the provided evidence. Buyers should confirm current pricing on the vendor website.
At a glance
- Best for
- Enterprise companies, Mid-market companies, Data engineering teams, AI and ML practitioners
- Key use cases
- AI Data Preparation, Full-Dataset Model Training, Near Real Time Inference, Enterprise Data Warehousing
- Integrations
- ODBC, JDBC, Python
- Official website
- Visit sqream official website
How AI is used
SQream is a data and analytics acceleration platform built natively for NVIDIA GPUs. It is designed to support the AI pipeline, including data preparation, model training, and inference at a petabyte scale.
The software is intended for mid-market and enterprise-level organizations, particularly those in data-heavy sectors such as finance, telecom, and manufacturing. It supports the ingestion and transformation of raw data into AI-ready pipelines, which may help teams train models on full datasets rather than smaller samples.
Buyers should confirm the technical expertise required for implementation and verify if their current hardware or cloud environment supports the GPU requirements needed for the platform.
Key Features
SQL on GPU
Supports ANSI-SQL syntax to execute complex queries and analytics on GPU hardware.
Petabyte-Scale Ingest
Designed to ingest and transform massive volumes of data without the need for sampling.
Automatic Data Compression
Supports a 5:1 data compression ratio for ingested data.
Role-Based Access Control (RBAC)
Allows administrators to define granular privileges for specific databases, schemas, and tables.
Linear Scalability
Supports scaling compute resources to match growing data and AI pipeline needs.
Use Cases
AI Data Preparation
Transforming raw data through cleaning, denormalization, and feature generation to create training pipelines.
Full-Dataset Model Training
Supports the development and fine-tuning of ML models using complete datasets instead of samples.
Near Real Time Inference
Deploying predictions at a massive volume with high throughput.
Enterprise Data Warehousing
Managing and analyzing large-scale data for business analysts and data scientists via SQL clients.
Integrations
- ODBC
- JDBC
- Python
FAQ
What is SQream used for?
- SQream is used to support the AI pipeline, specifically for preparing massive datasets, training machine learning models on full data, and running near real time inference.
Who is the target audience for SQream?
- It is designed for mid-market and enterprise companies in industries such as finance, telecommunications, manufacturing, and retail.
How is SQream priced?
- SQream uses an annual subscription model based on the number of GPUs used, though specific pricing requires a custom quote.
Source category: Data & Analytics
Source subcategory: Analytics & Reporting
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