13 AI feeds off data — and not just any data, but massive amounts of unstructured data that needs to flow instantly to GPUs for training and inference to enable today’s business-critical AI workloads. But here’s the catch: Those datasets are often not in one place. For both enterprises and service providers, they can be scattered across data centers, edge locations, and diverse public cloud environments, creating bottlenecks that stall data access, collaboration, governance, and, ultimately, AI innovation. Let’s take a closer look at this in a real-world scenario:Imagine a global research team racing to train an AI model for real-time medical diagnostics. Their training data is split between on-premises labs, cloud archives, and edge devices in hospitals. Every delay in moving data to GPUs slows discoveries and drives up costs. This is the kind of challenge Hammerspace and Scality have partnered to solve. By partnering to unify, protect, and accelerate access to data across any environment, Hammerspace and Scality enable AI pipelines, HPC workloads, and analytics jobs to run at full speed. The result? Faster insights, lower costs, and a foundation built for next-generation AI. How the joint solution works Hammerspace Data Platform orchestrates data across disparate storage systems by creating a single global namespace that unifies file and object data — including data stored on Scality RING and ARTESCA object storage solutions. Hammerspace uses standards-based protocols (NFS and SMB for files; S3 for objects) allowing applications and users to access the same dataset via any protocol without duplication or data copy sprawl. Hammerspace reads/writes directly to Scality’s object storage — without proprietary gateways or proxies — while metadata-driven policies automate data placement, movement, and protection. This lets organizations dynamically tier, replicate, or cache data between high-speed file tiers and cost-efficient object storage to optimize AI, HPC, and analytics performance. Key technical features of the Hammerspace + Scality AI solution Global data orchestration: Automatically moves unstructured data between Scality object storage and other storage tiers based on workload demands. Multi-protocol access: Enables simultaneous access to data via file protocols and native S3, simplifying application integration across AI, analytics, and HPC environments. Standards-based parallel file system architecture: Supports linear scaling in throughput and capacity as underlying infrastructure scales, without the complexity of proprietary clients or agents — ideal for large datasets typical in AI model training and inference. Non-disruptive integration: Works with existing storage hardware and network infrastructure without requiring application or workflow changes. Metadata-driven policies: Provides manual or automated, fine-grain control over data placement, movement, protection, and access to enable automation based on business context. What Scality + Hammerspace customers can achieve Unification of dispersed data: Consolidate unstructured data from edge, core, and cloud into a single, accessible global namespace. Accelerated workloads: Enable high-performance access for demanding AI, HPC, and analytics workloads, ensuring GPUs and compute resources are fully utilized. Automated data management: Leverage policy-based data orchestration to automate data placement and movement for optimized performance, cost, and compliance. Assured data protection & resilience: Benefit from Scality’s robust object storage with built-in immutability and high availability for comprehensive data protection. Elimination of data silos: Remove barriers between different storage systems and locations, making data an instantly accessible resource no matter where it resides. What AI-specific advantages does this provide? Accelerated AI model training and inference: By dynamically tiering data based on access patterns, large training datasets can reside efficiently on Scality’s scalable object storage while Hammerspace stages active datasets in high-performance NVMe cache or file storage for ultra-low latency processing. Multi-cloud and edge AI collaboration: Hammerspace orchestrates distributed datasets across on-premises, cloud, and edge environments with Scality acting as a reliable, secure object storage backend. This enables globally distributed teams and AI pipelines to share and collaborate on massive datasets in real time. Data lakes for ML pipelines: Scality provides exabyte-scale object storage for unifying data lakes, while Hammerspace abstracts and orchestrates access to these data lakes, streamlining ingestion, feature extraction, and analytics. Comprehensive data protection & security: The combined solution supports snapshots, replication, encryption, and ransomware defense mechanisms critical to protect sensitive AI training data. The bottom line: AI can’t wait for your data As AI adoption accelerates, so does the complexity of managing data at scale. Hammerspace and Scality give you the speed, reach, and protection to turn that challenge into your competitive advantage. With Hammerspace’s intelligent orchestration and Scality’s high-capacity, resilient object storage, organizations can keep compute fed with the right data at the right time, unify data across edge, core, and cloud into a single accessible view, and deliver performance, scale, and resilience in one streamlined platform.
