Sdx offers a compute‑to‑data platform that lets users run SQL queries and machine‑learning workloads directly on data stored in a third party’s environment, so the raw data never leaves its original location. By executing workloads on‑premise with secure enclaves, homomorphic encryption, and fine‑grained access controls, the platform preserves data ownership, compliance, and privacy while delivering encrypted results to analysts.
Funding
Funding not disclosed
Founders
Product
Problem
Organizations often need to analyze data owned by external parties, but transferring raw datasets raises privacy, security, and trust concerns, especially when regulations restrict data movement.
Solution
Sdx provides a compute-to-data platform that enables users to run SQL queries and machine‑learning algorithms directly on data residing in its original storage location. By deploying computation workloads on‑premise, the platform ensures that raw data never leaves the data owner's environment, preserving ownership and compliance. The system leverages privacy‑preserving techniques—such as secure enclaves and encrypted execution—to protect data during processing while delivering actionable insights to the requester. Results are returned in a controlled, encrypted format, allowing analysts to gain value without exposing sensitive information.
Target Audience
Primary customers are enterprises, data providers, and analytics teams that need to collaborate on sensitive datasets while maintaining strict data governance and regulatory compliance.
Features
- On‑premise execution engine that runs SQL and ML workloads against remote data sources without data extraction
- Secure enclave and homomorphic encryption support to guarantee confidentiality during computation
- Fine‑grained access controls and audit logs that enforce data owner policies
- Compatibility with common data storage systems and connectors for seamless integration
- Result encryption and controlled delivery mechanisms to prevent data leakage