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Making unstructured data AI-ready

Unlock the Full Potential of Your Unstructured Data

Transform unstructured data into actionable insights. 

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Transforming Unstructured Data Management

MetadataHub is a game-changer for organizations drowning in unstructured data. Whether it’s coming from sensors, microscopes, or satellites, the challenge isn’t just storing all that data—it’s figuring out how to extract value from it.

MetadataHub connects directly to storage systems like SMB, NFS, and S3, automatically extracting all types of metadata, including the critical embedded metadata that captures the content and context of your files. This ensures no critical insights are missed, making your data actionable and accessible for users and applications.

Think of MetadataHub as a dynamic, searchable repository that acts as a smart proxy for your data, eliminating the need to constantly recall full files. It integrates seamlessly with analytic tools, data lakes, and large language models (LLMs), delivering rich metadata without moving entire files. For sectors like life sciences, HPC, and manufacturing dealing with petabytes of research data or billions of sensor readings, this capability is transformative.

Comprehensive Metadata Extraction

Automatically captures and harmonizes metadata from diverse file formats, ensuring no critical insights are missed.

Smart Proxy Functionality

Reduces reliance on recalling full files, enabling seamless access to metadata for users and applications.

Automated Data Pipelines

Integrates with analytics and AI platforms like Snowflake, Databricks, KNIME, and large language models (LLMs) for immediate data usability.
Download the white paper "Revolutionizing Data Discovery and Orchestration"

Key Advantages of Automated Metadata Management

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Unlock the Hidden Value in Unstructured Data

  1. Automated Metadata Capture: Save up to 90% of data preparation time by automating metadata extraction at scale.
  2. Efficient Data Provisioning: Deliver metadata directly to applications without moving full files, reducing network and infrastructure loads by 30%.
  3. Enterprise Storage Optimization: Enable intelligent tiering and archiving policies based on file value, accelerating migration to cost-effective storage while maintaining accessibility.
  4. AI-Ready Data Management: Harmonized metadata transforms unstructured data into AI-ready assets, improving analytics and decision-making processes.
  5. Enhanced Data Accessibility: Streamline workflows and provide unified access across enterprise storage systems.
     

AI-Ready Data Workflows

MetadataHub solves critical enterprise challenges to prepare unstructured data for AI:

  • Diverse File Type Management: Standardizes metadata from numerous enterprise file formats
  • Large-Scale Data Processing: Efficiently handles millions or billions of files
  • Embedded Metadata Extraction: Captures and harmonizes context-rich metadata elements, ensuring data is usable for AI applications like RAG (Retrieval-Augmented Generation), machine learning, and analytics
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