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DataHub - A Metadata Platform for your Data and AI Stack
An open-source metadata platform to empower data discovery, data governance, and data lineage across your data and AI infrastructure.
Java
Added on 2025年6月26日10,777
Stars3,143
ForksJava
LanguageProject Introduction
Summary
DataHub is a modern, extensible metadata platform built to help organizations manage, discover, and understand their data and AI assets effectively. It provides a unified view and powerful tools for navigating complex data landscapes.
Problem Solved
In modern, complex data ecosystems, finding relevant data, understanding its context, ensuring quality, and maintaining governance are significant challenges. DataHub provides a central platform to solve these issues at scale.
Core Features
Unified Metadata Catalog
Collects metadata from diverse sources (databases, data lakes, BI tools, etc.) into a single, searchable catalog.
Data Discovery & Search
Provides powerful search capabilities to quickly find data assets based on keywords, tags, owners, and more.
Automated Data Lineage
Automatically maps and visualizes the flow of data from source systems to downstream applications, showing dependencies.
Data Governance & Ownership
Enables assigning owners, adding business glossary terms, tags, and documentation for robust data governance.
Tech Stack
Java
Kotlin
Python
React
TypeScript
Kafka
Elasticsearch
MySQL
Usage Scenarios
DataHub is applicable in various scenarios where robust data management, discovery, and governance are critical:
Empowering Data Discovery & Self-Service
Details
Enable analysts and scientists to intuitively search, understand, and trust the data assets available to them across the organization.
User Value
Reduces time spent searching for data, increases analytical productivity, and fosters data-driven decision making.
Enhancing Data Governance & Compliance
Details
Provides tools for documenting data assets, assigning ownership, managing business glossaries, and tracking data lineage to meet compliance requirements (e.g., GDPR, CCPA).
User Value
Ensures adherence to data policies, improves audit readiness, and reduces regulatory risk.
Streamlining Data Engineering Workflows
Details
Allows engineers to visualize dependencies between datasets and pipelines, simplifying impact analysis for changes, debugging data issues, and understanding system architecture.
User Value
Minimizes errors during pipeline modifications, speeds up development cycles, and improves system reliability.
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