DZone
Thanks for visiting DZone today,
Edit Profile
  • Manage Email Subscriptions
  • How to Post to DZone
  • Article Submission Guidelines
Sign Out View Profile
  • Post an Article
  • Manage My Drafts
Over 2 million developers have joined DZone.
Log In / Join
Refcards Trend Reports
Events Video Library
Refcards
Trend Reports

Events

View Events Video Library

Related

  • Simplify Data Management With Rimage’s AI-Powered Platform
  • How Artificial Intelligence and Data Management Interconnect
  • AIOps Being Powered by Robotic Data Automation
  • Architecting AI-Native Cloud Platforms: Signals to Insights to Actions

Trending

  • Spring Boot Done Right: Lessons From a 400-Module Codebase
  • Production-Grade RAG: Why Vector Search Isn't Enough (and How Hybrid Search Fills the Gaps)
  • Token Attribution Framework for Agentic AI in CI/CD
  • Reactive Ops to Autonomous Infrastructure: How Agentic AI Is Redefining Modern DevOps
  1. DZone
  2. Data Engineering
  3. AI/ML
  4. Simplifying Data Management With Hammerspace

Simplifying Data Management With Hammerspace

The control plane unifies access and orchestrates movement across infrastructure silos to simplify data management and optimize hybrid cloud storage costs.

By 
Tom Smith user avatar
Tom Smith
DZone Core CORE ·
Jan. 26, 24 · Analysis
Likes (1)
Comment
Save
Tweet
Share
3.3K Views

Join the DZone community and get the full member experience.

Join For Free

Data management complexity continues to grow as organizations adopt hybrid cloud strategies, manage greater data volumes, and implement emerging technologies like AI and machine learning. This places significant burdens on developers, engineers, and architects who get bogged down with tedious, manual data management tasks. 

Hammerspace aims to alleviate these challenges with its innovative data management platform. During the 53rd IT Press Tour, I spoke with David Flynn, Founder and CEO of Hammerspace, to understand how the platform simplifies data management for technical professionals. Here are the key takeaways that developers, engineers, and architects should know.

Unified Data Environment 

Hammerspace provides a unified hybrid cloud data management platform to discover, access, and share data assets across diverse infrastructures. According to David Flynn:

"Hammerspace elevates the file system above the infrastructure layer, bringing it into the orchestration plane. We assimilate just the metadata from existing storage systems without moving any data. So users get a single global namespace to access their files across all storage types and locations."

This unified environment eliminates data silos and fragmentation pain points for developers, engineers, and architects. They can now seamlessly access and work with data spread across storage tiers, clouds, and on-premise infrastructure.

Automated Data Management

Hammerspace automates tedious data management tasks for technical professionals using AI and machine learning algorithms. These capabilities handle various processes like:

  • Automatic data discovery and classification
  • Continuous data optimization across hot, warm, and cold storage
  • Predictive data caching based on access patterns
  • Data lifecycle management from production to archival storage  

According to Flynn, Hammerspace orchestrates the movement of data in the background without dependencies on applications or infrastructure. This automation enables developers, engineers, and architects to focus on core responsibilities instead of allocating valuable time to managing data.

Advanced Analytics and Insights

Hammerspace provides intuitive dashboards and detailed analytics into data usage across the hybrid environment. Technical teams can leverage these rich insights to:

  • Understand data usage patterns 
  • Identify redundant, obsolete, and trivial (ROT) data
  • Analyze storage utilization and identify optimization opportunities
  • Proactively plan infrastructure needs based on data growth
  • Monitor data workflows across infrastructure

These data analytics simplify infrastructure planning and help optimize hybrid cloud costs.  

Compliance and Security

Hammerspace offers extensive security and compliance capabilities to safeguard data. The platform enables granular role-based access control, encryption, and data masking to protect sensitive information. 

Moreover, Hammerspace helps maintain compliance with regulations like GDPR and CCPA when handling private customer data. Technical teams can define policies to restrict data movement for compliance. Customizable metadata tags also allow teams to classify sensitive data for appropriate handling per regulatory needs.

Easy Infrastructure Integration 

According to Flynn, Hammerspace seamlessly integrates with existing tools and infrastructure like GitHub, Jenkins, and cloud platforms. This means developers don’t need to migrate applications or data to leverage Hammerspace's orchestration capabilities.

The platform ingests metadata and serves as a control plane to direct data traffic appropriately between storage systems. All infrastructure remains untouched, apart from minor networking adjustments to route traffic through Hammerspace.

Hammerspace also supports standard data interfaces like NFS, SMB, and S3. This integration flexibility makes Hammerspace easily adaptable across toolchains.

Cost Optimization

Hammerspace optimizes infrastructure spending in several ways. The software automatically tiers data across storage types ranging from high-performance primary storage to low-cost cloud object storage. Hot data requiring low latency stays on flash, while inactive cold data gets moved to cheaper archival storage. Built-in deduplication and compression further reduce the consumption of expensive storage.

Additionally, Hammerspace provides detailed analytics into consumption costs across on-premise and cloud environments. Teams can identify waste from inefficient use, as well as opportunities to better align storage provisioning. Archival policies also automate the transition of aged data to inexpensive long-term object storage. The combined impact of these intelligent capabilities helps balance infrastructure expenses alongside performance and availability needs. By optimizing storage utilization, Hammerspace customers achieve up to 40% TCO savings.

Optimized for AI/ML Workloads

Modern applications like AI/ML workloads demand the ability to efficiently access large datasets during model development cycles. Hammerspace is purpose-built to address these emerging challenges with data orchestration targeted for AI/ML pipelines.

Key highlights powering simplified AI/ML workflows include:  

  • Unified data lake supporting clean separation of raw data from feature stores
  • Dynamic data caching predictive of the model needs, based on training cycles 
  • Automated data versioning, facilitating rapid iteration of models    
  • Continuous data optimization across high-performance and cost-effective storage
  • Single namespace and orchestration from edge to core to cloud

This AI/ML-focused data orchestration dramatically accelerates model development while optimizing infrastructure.

Redefining Data Management  

Hammerspace redefines data management for hybrid cloud environments. By decoupling storage from infrastructure, the company helps unify data access while coordinating movement behind the scenes. David Flynn summarized the vision:

"We believe data infrastructure should revolve around efficiently moving data based on how it needs to be consumed. Hammerspace makes this possible by becoming the orchestration control plane for a software-defined data environment across clouds."

By simplifying data management with intelligent orchestration, Hammerspace allows technical teams to work more efficiently. The platform overcomes key challenges around hybrid cloud adoption, infrastructure sprawl, and emerging workloads like AI/ML. Organizations no longer have to settle for disjointed data silos and fragmented user experiences. Hammerspace delivers a unified and automated approach to help developers, engineers, and architects optimize the data layer.

AI Data management Machine learning Data (computing)

Opinions expressed by DZone contributors are their own.

Related

  • Simplify Data Management With Rimage’s AI-Powered Platform
  • How Artificial Intelligence and Data Management Interconnect
  • AIOps Being Powered by Robotic Data Automation
  • Architecting AI-Native Cloud Platforms: Signals to Insights to Actions

Partner Resources

×

Comments

The likes didn't load as expected. Please refresh the page and try again.

  • RSS
  • X
  • Facebook

ABOUT US

  • About DZone
  • Support and feedback
  • Community research

ADVERTISE

  • Advertise with DZone

CONTRIBUTE ON DZONE

  • Article Submission Guidelines
  • Become a Contributor
  • Core Program
  • Visit the Writers' Zone

LEGAL

  • Terms of Service
  • Privacy Policy

CONTACT US

  • 3343 Perimeter Hill Drive
  • Suite 215
  • Nashville, TN 37211
  • [email protected]

Let's be friends:

  • RSS
  • X
  • Facebook