175 The backbone of every storage system is its metadata database — the system’s master catalog of all stored data. With storage requirements now frequently reaching hundreds of petabytes and beyond, the ability to efficiently manage and quickly access huge volumes of unstructured data has never been more critical. As these data volumes grow and become more siloed, businesses are finding it increasingly challenging to efficiently index, retrieve, and manage this information. The powerful integration of Scality RING and GRAU DATA’s MetadataHub offers an optimal solution for managing large volumes of unstructured data, streamlining its usability for AI model training. Specifically, MetadataHub transforms how unstructured data is accessed and managed by capturing both content and context. It enables faster data access, enhances visibility, and unlocks AI-ready workflows, making unstructured data management more accessible and actionable. Redefining Scale Learn how Scality RING uniquely manages metadata at scale in our new guide: Redefining scale: Why next-gen AI and cloud data demands multidimensional scaling DOWNLOAD PDF What is GRAU DATA’s MetadataHub? MetadataHub makes metadata management simple. It’s a powerful platform designed to extract and organize metadata, including embedded metadata, from a wide range of file types found in data-intensive industries such as research, life sciences, and satellite applications. It supports hundreds of file formats and is complemented by AI-powered recognition for images, audio, and text files, automating workflows and improving data pipelines for faster, higher-quality data. MetadataHub builds on RING’s powerful, extensible metadata functionality that allows users to search and retrieve data quickly based on rich content and context attributes, offering a much deeper and more detailed understanding of the data. This makes MetadataHub essential for organizations that rely on data-driven decision-making and need an efficient way to handle complex datasets. METADATADUB: How it works 1. Break silos and power faster model development with streamlined data retrieval and visibility across systems MetadataHub accelerates data retrieval, especially in environments like machine learning (ML) pipelines. In these scenarios, organizations need to assemble vast datasets based on specific parameters to train and test models. MetadataHub indexes data stored on Scality RING, allowing users to query datasets efficiently. By speeding up data extraction, MetadataHub shortens the time spent preparing data, enabling faster model development and deployment in ML workflows. In addition to Scality RING, MetadataHub also indexes data from other storage systems, including cloud environments and NAS systems. This cross-system indexing breaks down data silos and provides a unified view of an organization’s data, making it easier to search, organize, and manage data across various repositories. This holistic view is crucial for organizations managing hybrid-cloud environments. 2. Reduce storage costs by up to 30% with efficient data archiving and policy management Data lifecycle management is critical to modern data governance. MetadataHub allows organizations to establish automated archiving policies based on content and context, driving more intelligent and precise data management. By capturing all content and context, MetadataHub becomes a proxy for the file itself, providing deep insights into the data. With this understanding, most data — not just lower-priority or less frequently accessed data — can be moved from expensive low-latency storage to a cost-effective RING tier sooner, without impacting performance. This content-aware workflow maximizes storage resources far more effectively than traditional tiering and archiving methods, reducing storage costs by up to 30%. MetadataHub can integrate with existing unstructured data management solutions and improve tiering and archiving strategies, by automating how data is archived based on detailed content attributes. 3. Identify, manage, and share data efficiently across storage systems to ensure compliance MetadataHub enables organizations to meet governance requirements by ensuring that data is managed and shared according to established practices, such as FAIR (Findable, Accessible, Interoperable, Reusable) principles. By cataloging and organizing content and context, MetadataHub ensures that organizations can locate and govern data efficiently, applying necessary protections and controls based on how the data should be managed and shared. For example, if an organization needs to ensure compliance with internal governance rules or data-sharing practices under FAIR, MetadataHub can help identify data that needs to be curated or managed more effectively across Scality RING and other storage systems. 4. Capture content and context from unstructured data to power retrieval augmented generation (RAG) workflows Retrieval augmented generation (RAG) is an AI approach that improves the quality of generated outputs by retrieving relevant information from large datasets. MetadataHub’s ability to capture content and context from unstructured data makes it an ideal enabler for RAG workflows. By indexing vast amounts of data and making it easily retrievable, MetadataHub ensures that AI models can pull in the most relevant and accurate information during the generation process, enhancing both the precision and quality of AI-generated content. This capability is particularly useful for applications requiring highly contextual responses, such as natural language processing (NLP) and generative AI. 5. Improve the quality of AI/ML model training with automated data labeling MetadataHub significantly reduces the manual effort required to label unstructured data by automatically tagging data based on content and context. This capability is crucial for AI retrieval augmented generation (RAG) and machine learning, where labeled data is essential for training accurate models. By automating metadata extraction and harmonization, MetadataHub ensures that AI models work with cleaner, more relevant datasets, resulting in better predictions and outcomes. In addition, MetadataHub supports data provenance and transparency, ensuring that the origins, movement, and transformation of data are well-documented throughout its lifecycle. This is critical for ensuring trust and governance in AI models, particularly in regulated industries. Unlock your data’s potential with MetadataHub and Scality RING GRAU DATA’s MetadataHub enhances the already powerful capabilities of Scality RING, providing organizations with a comprehensive solution for indexing, retrieving, and managing large datasets. Whether streamlining ML workflows, improving data visibility across storage systems, optimizing data archiving, or ensuring governance compliance, MetadataHub empowers organizations to unlock the full potential of their data. By integrating MetadataHub with Scality RING, enterprises can transform their data into a strategic asset — driving efficiency, reducing costs, and ensuring compliance in an increasingly complex data landscape. Related content you may be interested in: Use case: Extreme performance for AI workloads Scality RING product page Learn how Scality RING uniquely manages metadata at scale in our new guide: Redefining scale: Why next-gen AI and cloud data demands multidimensional scaling
