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Average Ratings 0 Ratings
Description
Hammerspace innovatively leverages the local NVMe storage embedded within GPU servers, converting it into a high-performance, shared storage tier designed specifically for large-scale AI training and checkpointing workloads. This approach eliminates bottlenecks inherent in legacy storage systems that struggle to keep GPUs fully utilized, while significantly reducing power consumption and external storage expenses. The platform’s parallel file system architecture supports massive scalability, allowing data to be served simultaneously to thousands of GPU nodes with minimal latency. Hammerspace integrates seamlessly with existing Linux storage servers and supports hybrid cloud environments, enabling data orchestration between on-premises and cloud infrastructure. It delivers record-setting performance validated by MLPerf benchmarks, proving its efficiency for demanding machine learning workloads. Customers such as Meta and Los Alamos National Laboratory trust Hammerspace to optimize their AI data pipelines and infrastructure investments. With quick setup and intuitive management, Hammerspace helps organizations accelerate AI projects while reducing operational complexity. By transforming underutilized storage into a powerful resource, Hammerspace drives cost savings and faster innovation.
Description
Conventional techniques for integrating mainframe data, such as ETL, data warehouses, and connector development, are increasingly inadequate in terms of speed, accuracy, and efficiency in today’s business landscape. As the amount of data generated and stored on mainframes continues to surge, these outdated methods fall further behind. Data virtualization emerges as the solution to bridge this growing divide, automating the accessibility of mainframe data for developers and applications alike. This approach allows organizations to discover and map their data just once, after which it can be easily virtualized and reused across various platforms. Ultimately, this capability enables your data to align with your business goals and aspirations. By leveraging data virtualization on z/OS, organizations can simplify the complexities associated with mainframe resources. Moreover, data virtualization facilitates the integration of data from numerous disparate sources into a cohesive logical repository, significantly enhancing the ability to connect mainframe information with distributed applications. This method also allows for the enrichment of mainframe data by incorporating insights from location, social media, and other external datasets, promoting a more comprehensive understanding of business dynamics.
API Access
Has API
API Access
Has API
Integrations
Amazon Web Services (AWS)
Azure Marketplace
Google Cloud Platform
IBM Cloud
Microsoft Azure
Integrations
Amazon Web Services (AWS)
Azure Marketplace
Google Cloud Platform
IBM Cloud
Microsoft Azure
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
No price information available.
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Hammerspace
Founded
2018
Country
United States
Website
hammerspace.com
Vendor Details
Company Name
Rocket
Founded
1990
Country
United States
Website
www.rocketsoftware.com/product-categories/data-virtualization
Product Features
Data Management
Customer Data
Data Analysis
Data Capture
Data Integration
Data Migration
Data Quality Control
Data Security
Information Governance
Master Data Management
Match & Merge
Disaster Recovery
Administration Policies
Bare-Metal Recovery
Encryption
Failover Testing
Flexible Data Capture
Multi-Platform Support
Multiple Data Type Support
Offline Storage