Benefits
- Consolidate Unstructured Data in a Single Platform: Consolidate unstructured data into a Data Platform to simplify unstructured data management across its lifecycle.
- Effortlessly Discover and Access Data: Gain real time, self-service access to your data, with the ability to search across all data sources.
- Optimize Infrastructure Utilization: Reduce infrastructure load with intelligent, metadata-driven workflows.
- Automate Data Pipelines: Accelerate research and analytics with automated data pipelines and data orchestration.
Unlock Hidden Value in Massive Unstructured Datasets
Research organizations, HPC applications, and companies with AI initiatives generate massive unstructured data – billions of files and objects – that are siloed in disparate storage systems. Finding and managing this data is incredibly difficult, and extracting insights from this data to turn it into business value is even harder.
MetadataHub is an intelligent metadata repository that automatically captures and maintains rich content and contextual metadata from files, including specialized scientific and technical formats like microscopy, genomics, and satellite data. CIOs, CDOs, data scientists, and storage administrators rely on MetadataHub to transform unstructured data into valuable insights and gain transparency across all unstructured data sources. By unlocking content and context, MetadataHub accelerates discovery, enhances operational efficiency, supports strategic decision-making, and delivers better-quality data across the enterprise.
Hammerspace is a high-performance data platform that consolidates file and object data while automating data orchestration across hybrid and multi-cloud environments, ensuring data is available wherever it’s needed.
Together, Hammerspace and MetadataHub provide a solution for storing, managing, and extracting insights from massive unstructured data sets
How MetadataHub Works
Organizations face significant challenges in extracting insights from unstructured data scattered across diverse storage systems. MetadataHub transforms this process by automatically harvesting both content and embedded metadata from files, capturing contextual intelligence critical for ensuring data quality and driving analytics. The extracted metadata is aggregated into a centralized repository, creating compact data proxies that are just 1/1000th the size of the original files. Once these proxies are created, original files can be safely moved to low-cost archive storage, as the metadata contains all essential content needed for most data operations. These intelligent proxies can be queried and seamlessly provisioned to the tools users already use, enabling intuitive self-service access to critical data while preserving existing workflows. This capability automates workflows and reduces data preparation time by 90%.
While original files remain accessible when needed, organizations can leverage these efficient proxies to work directly with metadata, dramatically reducing GPU, CPU, and network resource consumption while eliminating unnecessary data transfers by up to 90%. Through a unified global query layer, teams can rapidly provision data for advanced analytics and AI workflows, significantly lowering infrastructure costs and accelerating time to insight.