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What is Hammerspace?

Hammerspace is an innovative storage solution that unlocks the vast amounts of fast local NVMe storage inside GPU servers, turning what was previously stranded capacity into a new ultra-fast storage tier that accelerates AI training and checkpointing. Designed to overcome the limitations of expensive and complex legacy storage systems, Hammerspace enables consistently high GPU utilization by providing low-latency, parallel access to data across thousands of servers simultaneously. The platform’s global data architecture is built on standard NFS protocols, eliminating the need for proprietary clients and simplifying integration with existing Linux storage servers and hybrid cloud environments. By orchestrating data movement between on-premises and cloud infrastructure, Hammerspace supports flexible AI workflows that maximize resource usage and minimize power consumption. Hammerspace has been proven through MLPerf benchmarks to deliver scalable, linear performance gains, dramatically speeding up checkpointing times by 10 to 100 times. Trusted by industry leaders like Meta and Los Alamos National Laboratory, it helps reduce external storage costs, power usage, and rack space requirements. The platform’s intuitive interface and rapid deployment allow organizations to bring petabytes of capacity online within days and start accelerating AI workloads immediately. Its hyperscale NAS architecture ensures consistent throughput and low latency at scale, critical for large machine learning projects. By transforming idle GPU server storage into a shared, protected resource, Hammerspace redefines cost efficiency and performance in AI infrastructure. This technology empowers data-driven organizations to innovate faster, reduce their energy footprint, and optimize their AI investment returns.

What is Dremio?

Dremio offers rapid query capabilities along with a self-service semantic layer that interacts directly with your data lake storage, eliminating the need to transfer data into exclusive data warehouses, and avoiding the use of cubes, aggregation tables, or extracts. This empowers data architects with both flexibility and control while providing data consumers with a self-service experience. By leveraging technologies such as Apache Arrow, Data Reflections, Columnar Cloud Cache (C3), and Predictive Pipelining, Dremio simplifies the process of querying data stored in your lake. An abstraction layer facilitates the application of security and business context by IT, enabling analysts and data scientists to access and explore data freely, thus allowing for the creation of new virtual datasets. Additionally, Dremio's semantic layer acts as an integrated, searchable catalog that indexes all metadata, making it easier for business users to interpret their data effectively. This semantic layer comprises virtual datasets and spaces that are both indexed and searchable, ensuring a seamless experience for users looking to derive insights from their data. Overall, Dremio not only streamlines data access but also enhances collaboration among various stakeholders within an organization.

Media

Media

Integrations Supported

Azure Marketplace
Google Cloud Platform

Integrations Supported

Azure Marketplace
AccessOwl
Apache Iceberg
Apache Superset
Codd AI
Cyral
DashboardFox
DataClarity Unlimited Analytics
Emgage
HPE Ezmeral
Hex
Looker
Microsoft Power BI
Microsoft Power Query
Privacera
PuppyGraph
SQL
Yurbi
data.world

API Availability

API Availability

Pricing Information

Pricing not provided

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

Not specified

Training Options

Documentation Hub
Online Training

Training Options

Not specified

Company Facts

Organization Name

Hammerspace

Date Founded

2018

Company Location

United States

Company Website

hammerspace.com

Company Facts

Organization Name

Dremio

Date Founded

2015

Company Location

United States

Company Website

www.dremio.com

Categories and Features

Data Management

Not specified

Data Virtualization

Not specified

Disaster Recovery

Not specified

Categories and Features

Big Data

Not specified

Data Engineering

Not specified

Data Lake

Not specified

Data Lineage

Not specified

Data Virtualization

Not specified

Data Warehouse

Not specified

Query Engines

Not specified

Semantic Layer

Not specified

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