DataHub: The Open-Source Metadata Platform Powering the Modern Data Stack

In today’s data-driven landscape, organizations face unprecedented challenges managing the exponential growth of data assets. As data ecosystems become increasingly complex—spanning multiple cloud platforms, data lakes, warehouses, and BI tools—teams struggle to discover, understand, and trust their data. This “metadata crisis” has emerged as a significant bottleneck for data-driven decision making.

LinkedIn’s DataHub has emerged as a powerful solution to this challenge, offering an open-source metadata platform specifically designed for the modern data stack. Unlike traditional metadata repositories focused primarily on technical cataloging, DataHub reimagines metadata management as a dynamic, social, and intelligent platform that brings together technical metadata, business context, governance, and collaboration in one unified experience.

This comprehensive guide explores how DataHub is transforming how organizations manage metadata, enabling data discovery, understanding, and trust at scale.

Traditional metadata approaches focused primarily on technical documentation of database schemas and ETL processes. As data ecosystems evolved, these approaches proved insufficient for several reasons:

1. Scale and Distribution Challenge: Data now spans dozens of systems with different metadata models
2. Contextual Knowledge Gap: Technical metadata alone doesn’t capture business meaning
3. Passive Documentation Problem: Static metadata quickly becomes outdated
4. Collaboration Void: Traditional catalogs don’t facilitate knowledge sharing

DataHub represents the third generation of metadata platforms, learning from LinkedIn’s experience building internal tools like WhereHows and addressing these challenges with a fundamentally new approach.

At its foundation, DataHub employs a unique architecture designed for flexibility, scalability, and integration with the modern data stack.

DataHub’s metadata model is built around key entities that represent the data ecosystem:

• Datasets: Tables, views, or other data containers
• Dashboards: Business intelligence visualizations
• Data Pipelines: ETL/ELT processes
• Data Jobs: Scheduled workflow tasks
• Users & Groups: People and organizations interacting with data
• Tags & Glossary Terms: Business context and classifications
• Domains: Organizational ownership areas

The relationships between these entities are captured through a graph model, enabling powerful traversal and discovery capabilities.

DataHub employs a modular, layered architecture:

1. Metadata Storage Layer: Manages persistent storage of metadata
2. Metadata Service Layer: Provides APIs for metadata access and manipulation
3. UI/Application Layer: Delivers the user experience for interaction

This separation of concerns enables flexibility in deployment and scaling each component independently.

DataHub exposes a powerful GraphQL API that enables:

# Example GraphQL query retrieving dataset information and its upstream lineage
query {
  dataset(urn: "urn:li:dataset:(urn:li:dataPlatform:bigquery,analytics.users,PROD)") {
    name
    description
    platform {
      name
    }
    upstreamLineage {
      upstreams {
        dataset {
          name
          platform {
            name
          }
        }
      }
    }
    tags {
      tags {
        tag {
          name
        }
      }
    }
    ownership {
      owners {
        owner {
          ... on CorpUser {
            username
            info {
              email
            }
          }
        }
      }
    }
  }
}

This API-first approach enables:

• Custom application development on top of DataHub
• Integration with existing tools and workflows
• Programmatic metadata management
• Flexibility for various use cases beyond the built-in UI

DataHub provides a Google-like search experience across all metadata, enabling users to:

• Find datasets, dashboards, and other assets through keyword search
• Filter by multiple dimensions (platform, owner, domain, etc.)
• Discover related assets through graph relationships
• Save and share search results

The search functionality is powered by Elasticsearch, providing fast, relevant results even across millions of metadata entities.

One of DataHub’s most powerful features is its comprehensive lineage capabilities:

• Column-Level Lineage: Track how specific fields transform across systems
• Multi-Hop Lineage: Follow data through complex transformation chains
• Impact Analysis: Understand downstream dependencies
• Cross-Platform Visibility: Trace data across different technologies

This lineage information helps teams understand data provenance, assess the impact of changes, and debug data issues more effectively.

DataHub bridges technical metadata with business meaning through:

• Business Glossary: Define standard business terms and concepts
• Domains: Group related assets by business function
• Tags: Flexible labeling system for classification
• Term-to-Data Mapping: Connect glossary terms to actual data assets

This semantic layer ensures everyone speaks the same language about data, reducing misunderstandings and enabling business users to find relevant data more easily.

DataHub provides robust capabilities for establishing clear ownership and governance:

• Ownership Assignment: Designate owners for any data asset
• Role-Based Governance: Define who can edit metadata
• Assertion Framework: Track claims about data quality and trustworthiness
• Audit Trail: Log all changes to metadata

These features help organizations establish clear accountability for data assets and implement effective governance processes.

DataHub introduces social elements to metadata management:

• Activity Feed: View recent changes to data assets
• @Mentions: Tag colleagues in conversations about data
• Reactions: Provide quick feedback on descriptions or questions
• Conversations: Discuss data issues within the metadata context

These features transform metadata from static documentation into a living, collaborative knowledge base.

DataHub stands out for its extensive integration with modern data tools and platforms.

DataHub provides out-of-the-box connectors for popular platforms:

Data Warehouses & Databases:

• Snowflake
• BigQuery
• Redshift
• PostgreSQL
• MySQL
• Oracle
• SQL Server

BI & Analytics Tools:

• Looker
• Tableau
• PowerBI
• Metabase
• Superset

Data Processing & Orchestration:

• Airflow
• dbt
• Glue
• Spark
• Kafka

ML & AI Platforms:

• Sagemaker
• MLflow
• Kubeflow

DataHub’s flexible ingestion framework supports various methods:

• Push-Based API: Systems can push metadata updates directly
• Pull-Based Connectors: DataHub can extract metadata on a schedule
• Event-Based Integration: Real-time metadata capture through events
• Custom Sources: Extend with custom connectors for proprietary systems

This flexibility ensures comprehensive metadata coverage regardless of where data lives.

For teams embracing DevOps principles, DataHub supports a “metadata as code” approach:

# Example YAML configuration for dataset metadata
platform: bigquery
dataset_name: analytics.customer_orders
env: PROD
description: "Daily aggregated customer order information"
tags:
  - name: PII
  - name: GDPR_SENSITIVE
owners:
  - username: jsmith
    type: DATAOWNER
  - username: data_platform
    type: TECHNICAL_OWNER
schema:
  fields:
    - fieldPath: customer_id
      type: STRING
      description: "Hashed customer identifier"
      tags:
        - name: PERSONAL_DATA
    - fieldPath: order_total
      type: DECIMAL
      description: "Total order amount in USD"

This approach enables:

• Version control for metadata
• CI/CD pipeline integration
• Automated testing and validation
• Consistent governance across environments

DataHub offers multiple deployment options to fit various organizational needs:

1. Docker Compose: Quickest way to get started for testing and small deployments git clone https://github.com/datahub-project/datahub.git cd datahub docker-compose -p datahub up
2. Kubernetes: Production-grade deployment using Helm charts helm repo add datahub https://helm.datahubproject.io/ helm install datahub datahub/datahub
3. Cloud-Native Services: Leverage managed services for components
   • Use Amazon RDS or Aurora for MySQL
   • Use Amazon Elasticsearch Service
   • Use Amazon S3 for document storage
   • Deploy application servers on EKS or ECS
4. SaaS Option: Acryl Data offers a managed DataHub service

For a typical mid-sized organization, consider these baseline resources:

• Compute: 4-8 CPUs for application servers
• Memory: 16-32GB RAM across services
• Storage: 50-200GB for metadata database
• Elasticsearch: 2-3 nodes with 4GB RAM each

These requirements scale based on metadata volume and user activity.

A successful DataHub implementation requires a thoughtful ingestion strategy:

1. Start with High-Value Sources:
   • Begin with core data warehouses and lakes
   • Add primary BI dashboards and reports
   • Include key data pipelines
2. Implement Regular Refresh Cycles:
   • Schedule metadata refreshes based on change frequency
   • Use change detection to minimize processing
   • Consider real-time ingestion for critical systems
3. Integrate with Development Workflows:
   • Capture metadata during CI/CD processes
   • Update lineage when pipelines are deployed
   • Generate documentation from code

Technical implementation is only half the battle. Successful metadata platforms require organizational adoption:

1. Identify Champions: Find advocates in different teams
2. Start Small: Begin with specific use cases and teams
3. Show Value: Demonstrate time savings for data discovery
4. Integrate with Workflows: Embed DataHub in existing processes
5. Measure Success: Track usage metrics and user feedback

Organizations that treat DataHub as a product rather than a project tend to see higher adoption rates and sustained value.

A global financial services company implemented DataHub to solve chronic data discovery problems:

Challenge: Analysts spent 30% of their time just finding relevant data across dozens of systems.

Solution: Deployed DataHub with connectors to Snowflake, Tableau, and dbt.

Implementation:

• Ingested metadata from 5,000+ tables and 200+ dashboards
• Integrated with identity management for automatic ownership
• Created business glossary with 300+ financial terms
• Trained search champions in each business unit

Results:

• 60% reduction in time spent searching for data
• 40% decrease in duplicate data pipeline creation
• 90% increase in data documentation completeness
• Improved cross-team collaboration on analytics

A healthcare provider used DataHub to address data governance requirements:

Challenge: Needed to document data lineage for HIPAA compliance and identify all assets containing protected health information (PHI).

Solution: Implemented DataHub with emphasis on lineage and classification.

Implementation:

• Created custom tags for different PHI categories
• Implemented automated classification through pattern recognition
• Built lineage across ETL processes and reporting systems
• Integrated with access control systems

Results:

• Complete audit trail for PHI data flows
• 70% reduction in compliance reporting effort
• Identified and remediated unauthorized PHI usage
• Streamlined auditor reviews with comprehensive documentation

DataHub’s frontend is built with React and can be customized:

• Create organization-specific landing pages
• Add custom visualizations for metadata
• Implement specialized search experiences
• Integrate with internal tools and portals

The metadata model can be extended to accommodate specialized needs:

{
  "type": "record",
  "name": "DataQualityCheckClass",
  "namespace": "com.company.datahub.aspect",
  "doc": "Data quality check metadata",
  "fields": [
    {
      "name": "checkType",
      "type": "string",
      "doc": "Type of data quality check"
    },
    {
      "name": "executionTime",
      "type": "long",
      "doc": "Time check was performed in milliseconds since epoch"
    },
    {
      "name": "success",
      "type": "boolean",
      "doc": "Whether the check passed"
    },
    {
      "name": "metrics",
      "type": {
        "type": "map",
        "values": "double"
      },
      "doc": "Metrics associated with this check"
    }
  ]
}

This extensibility allows organizations to track domain-specific metadata beyond what’s available out-of-the-box.

DataHub can integrate with enterprise security systems:

• SAML, OIDC, and LDAP integration
• Role-based access control
• Custom authorization policies
• Audit logging for compliance

While both address metadata management, they differ significantly:

• Origin & Focus: Atlas was designed for Hadoop ecosystems, while DataHub targets modern cloud data platforms
• Architecture: Atlas uses a JanusGraph backend, while DataHub employs a more modular approach
• User Experience: DataHub emphasizes search and collaboration, while Atlas focuses on governance
• Integration: DataHub has stronger connectors for cloud data warehouses and modern BI tools

Both originated from tech companies (LinkedIn and Lyft) but took different approaches:

• Architecture: Amundsen uses Neo4j as its primary datastore, while DataHub has a more flexible storage layer
• Data Model: DataHub offers a more comprehensive and extensible metadata model
• Search: Both provide strong search capabilities but with different indexing approaches
• Community: DataHub has gained stronger community momentum and more frequent releases

Compared to proprietary platforms like Alation or Collibra:

• Cost: DataHub is open-source with no licensing fees
• Extensibility: More flexible for customization and integration
• Time-to-Value: Can be deployed more quickly for basic use cases
• Support: Relies on community support or commercial partners like Acryl Data
• Enterprise Features: May require more configuration for advanced governance needs

The DataHub project continues to evolve rapidly, with several exciting developments on the horizon:

Upcoming releases will strengthen data quality capabilities:

• Native integration with Great Expectations and other quality tools
• Quality metrics visualization and trending
• Anomaly detection and alerting
• Quality-based search filtering

Machine learning is being applied to metadata management:

• Automated tagging and classification
• Smart recommendations for related data
• Natural language processing for improved search
• Anomaly detection in lineage and usage patterns

The metadata model is expanding to cover more data assets:

• Feature stores and ML models
• APIs and data services
• Code repositories and applications
• Streaming topics and events in more detail

Just as application observability has transformed DevOps, metadata observability is emerging:

• Real-time metadata change detection
• Alerting on lineage changes
• SLOs for metadata freshness
• Proactive data issue identification

DataHub represents a significant evolution in metadata management, addressing the unique challenges of modern data ecosystems. By combining technical metadata with business context, governance capabilities, and collaboration features, it transforms how organizations discover, understand, and trust their data assets.

As data ecosystems continue to grow in complexity, platforms like DataHub will become increasingly critical infrastructure for data-driven organizations. The open-source nature of the project, combined with its growing community and commercial support options, makes it a compelling choice for organizations of all sizes.

Whether you’re struggling with data discovery, implementing data governance, or building a data mesh architecture, DataHub provides the metadata foundation needed to succeed in today’s data-rich environment.

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