Hammerspace provides an automated data orchestration system that unifies and manages unstructured data across edge locations, data centers, and public cloud environments using a standards-based parallel file system architecture. This technology eliminates data silos, enabling organizations to optimize resource utilization and accelerate access to critical data for AI and high-performance computing applications.
Funding
$56.7M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
PVFounders
Product
Problem
Organizations struggle with data silos across edge locations, data centers, and clouds, hindering efficient resource utilization and access to critical unstructured data required for AI, HPC, and other data-intensive applications. Managing and moving this data between disparate storage systems is complex, time-consuming, and expensive.
Solution
Hammerspace provides a software-defined global data platform that unifies unstructured data across heterogeneous storage environments, creating a single global namespace accessible from any location. The platform leverages a standards-based parallel file system (pNFS) architecture to deliver high-performance data access for AI/ML, HPC, and GPU computing workloads. Metadata-driven data orchestration automates data placement and movement based on policies, optimizing resource utilization and accelerating workflows. This eliminates data silos, improves collaboration, and reduces the cost and complexity of managing distributed unstructured data.
Target Audience
Hammerspace targets organizations in industries such as electronic design automation, financial services, game development, high-performance computing, life sciences, media and entertainment, and the public sector that require high-performance, globally accessible unstructured data for AI, HPC, and distributed workflows.
Features
- Single global namespace spanning on-premises, edge, and cloud storage
- Standards-based parallel file system (pNFS) for high-performance data access
- Automated data orchestration based on metadata and policies
- Support for heterogeneous storage environments, including NAS, object storage, and cloud storage
- Linear scalability from a few nodes to thousands, supporting exabytes of capacity
- Integration with GPU computing environments for AI/ML workloads
- Data protection features, including snapshots, clones, versioning, and undelete
- REST API for integration with existing infrastructure and applications