DataHub is a metadata management platform designed to unify technical, operational, and business context across diverse data ecosystems. By utilizing a graph-based metadata model and an event-driven ingestion architecture, it creates a centralized source of truth that maps complex data relationships, lineage, and ownership. This foundational framework enables organizations to maintain a synchronized view of their data landscape, supporting both human-led discovery and automated data operations.
The platform distinguishes itself through its focus on grounding artificial intelligence and autonomous agents in verified enterprise context. It provides specialized capabilities to inject provenance-aware lineage, business definitions, and quality signals into AI prompts, ensuring that generated insights are accurate and trustworthy. Through a policy-as-code governance engine, it enforces access controls and compliance rules directly within the metadata graph, allowing for programmatic oversight of data assets across hybrid environments.
Beyond its core identity, the project offers a comprehensive suite of tools for data discovery, observability, and lifecycle management. It includes features for automated lineage extraction, impact analysis, and semantic search, enabling users to navigate data dependencies and resolve quality issues efficiently. The platform also supports collaborative workflows, allowing teams to manage business glossaries, certify data assets, and automate access requests through integrated communication channels.
DataHub is built to scale, utilizing a distributed architecture that allows storage, search, and graph processing layers to operate independently. It provides standardized interfaces and a bridge-based connector framework to facilitate integration with heterogeneous data sources and external AI agent frameworks.
