Top 6 Open-Source Data Governance Tools 

Open source data governance tools with data cataloging capabilities help collect, process, and maintain metadata, serving as a central repository for tracking data operations. Organizations use these tools in data security posture management (DSPM), data discovery, data lineage, and data quality initiatives. Based on user experiences posted on review platforms and product features, here are the top 6 examples:

Feature comparison

All tools offer searching functionality to list data assets.

Feature descriptions:

• Specification-based – provides documentation on metadata managed within an application or environment to allow efficient data discovery and federating data catalogs.
• Data quality –  identifies errors across data sets, often through processes such as:
  • Data profiling
  • Data validation, and standardization
  • Incident reporting
• Column-level lineage – provides data lineage with granularity at the column level.

Market presence and pricing comparison

Vendor selection & sorting:

• GitHub stars: 500+
• GitHub contributors: 30+
• Sorting: Tools are sorted based on GitHub stars in descending order.

Disclaimer: Insights (below) come from user experiences shared in Reddit, and G2.

DataHub

DataHub is an open-source platform for sensitive data discovery and governance developed by Acryl Data and LinkedIn. It is also available as a commercial license as a cloud-hosted SaaS solution. 

It supports over 70 native integrations:

• Data warehousing and databases (e.g., Snowflake, BigQuery, Redshift, Postgres, and MySQL)
• Business intelligence tools (e.g., Looker, Power BI, Tableau)
• Identity and access management tools (e.g., Okta, LDAP)
• Data lakes/storage solutions  (e.g., S3 and Delta Lake)

DataHub offers on-prem and cloud versions:

• DataHub Cloud extends governance functionality with customized workflows and computational governance features, making it suitable for organizations needing robust approval mechanisms and automated governance.
• DataHub (on-premises) focuses on foundational governance capabilities like ownership management, providing core functionality for smaller teams or less complex environments.

DataHub enterprise features across DataHub and DataHub Cloud:

Pros:

• Integrations: Strong support for tools like MySQL, Elasticsearch, Kafka, and APIs like GraphQL and OpenAPI. It supports robust ecosystem partnerships (e.g., Delta Lake).
• Customizability: Provides flexible metadata models and ingestion frameworks.
• Established community: Active development led by LinkedIn, over 500 contributors, and a supportive Slack community.
• Developer-friendly: GraphQL endpoints and open API make integrations easier for engineering teams.

Cons:

• Complex setup: Requires infrastructure such as MySQL, Kafka, Elasticsearch, and potentially Neo4j, which can be resource-intensive.
• Learning curve: Setting up and managing at scale may require expertise in handling dependencies and Kubernetes (EKS).

OpenMetadata

OpenMetadata is a single catalog that compiles metadata from all sources and displays it to the user according to their needs. 

Distinct feature: It has a less complex architecture compared to tools like Egeria and Apache Atlas. OpenMetadata can be integrated with current toolchains using REST APIs. 

Key features:

• Include descriptions to your metadata: To help you determine the validity and owner details when necessary, you can define the owner and add descriptions to the data.
• Data profiling: View the details for each data column and check the null or non-null value counts.
• Extensive metadata connectors: 80+ connectors to ingest metadata from databases, BI tools, data lakes, and warehouses, including:
  • Database connectors: Oracle, MySql, Snowflake, Hive, and many more can be integrated.
  • Dashboard connectors: Superset, Tableau, etc.
  • Pipeline connectors: Prefect, Airflow, etc.

Pros:

• Architecture: Avoids Kafka by default, reducing dependency complexity. Fully API-driven design allows seamless integration.
• No-code editor: A drag-and-drop no-code editor can be used to enhance the lineage extracted from machine metadata.
• Broad connectors: Athena, Superset, and Dagster, with ongoing additions.

Cons:

• Maturity: Still growing compared to DataHub; some pipelines (e.g., for Dagster) are not yet fully integrated.

LakeFS

LakeFS is a data version control tool for your data lake. It enhances data governance by offering a transparent version of data history and the capability to monitor changes.

It turns your object storage into a repository similar to Git. It lets you handle your data lake in a similar method that you handle your code. From complex ETL tasks to data science and analytics, you can create repeatable data lake activities.

Storage service support: 

• lakeFS supports Google Cloud Storage, AWS S3, and Azure Blob Storage.
• It integrates with key modern database frameworks, including Spark, Hive, AWS Athena, DuckDB, and Presto, and it is API-compatible with S3.

Pros:

• Atomic (single unit) data operations:
  • Prevents partial or invalid data reads effectively.
  • Enables idempotent (a type of HTTP method that can be called multiple times with the same input parameters while producing the same result) ETL tasks for data lake activities.
• Compatibility:
  • API-compatible with AWS S3, making it easy to use existing S3 tools and SDKs.
• Implementation: 
  • Works at the object level, making it suitable for both structured and unstructured data. 
  • Useful for debugging by analyzing data states when errors occurred.

Thus, LakeFS would be an ideal solution for:

• Teams looking for data versioning in data pipelines.
• Organizations using AWS S3 or compatible storage solutions.
• Data teams focused on ETL processes, debugging, and historical data analysis.

Cons:

• Lack of deletion capabilities: Deleting files is currently unsupported, leading to potential GDPR compliance issues and increased storage costs.
• No deduplication for data files: This increases storage redundancy.
• Lacks federated identity support: This is essential in enterprise environments.

Amundsen

Amundsen is a metadata and data discovery engine initially developed by Lyft. 

 It works by indexing data resources (such as tables, dashboards, streams, etc.) and enabling a page-rank type search that is based on usage patterns (for example, tables that receive a lot of queries appear first, followed by tables that receive fewer queries). 

Distinct feature: Amundsen prioritizes pull-based integration and simplicity, which makes it lighter for quick deployments.

Thus, for companies looking for simplicity and a searchability focus without the need for real-time updates or detailed lineage Amundsen is an ideal solution. If your team needs enterprise-grade features and a deeper metadata lineage other tools like DataHub might be a better option.

Pros:

• High column details for data discovery: Visualization of Hive/Redshift table columns with an optional statistics display.

  

• Ease of setup: Easier to install and deploy compared to some alternatives like DataHub.
• Search feature: Strong search capabilities using Elasticsearch.

Cons:

• Lineage support: Offers lineage but is less feature-rich than solutions, particularly for dataset-to-dataset lineage visualization and navigation.
• Push model: Relies mainly on pull-based integration, which might not work well for all workflows requiring real-time metadata updates.
• No SLAs and support packages: SLAs and support packages aren’t currently available.

Egeria

Egeria is an open-source project that allows organizations to share and manage data across their organization. Egeria relies upon the OpenLineage standard for data lineage.

Egeria defines the open metadata standard schema for 800+ types of metadata required by enterprises to manage their digital resources. 

To enable tools and metadata repositories to share and exchange metadata using these open standards, Egeria implements open APIs, frameworks, connectors, and interchange protocols for these standard types.

Focus: Egeria is enterprise-focused and targets massive amounts of metadata in large companies. For teams who require a highly automated, integrated solution for platform-to-platform information exchange, it might be an ideal option.

Pricing:

• Free and open-source: Egeria is community-maintained, with no usage or data source limitations.
• Paid services: Contractors offer mentorship, custom development, and deployment services.
• Commercial option: Integrated into IBM Watson Knowledge Catalog, available as a SaaS product.

Pros:

• Extensive connectivity: Supports a wide range of integrations, including APIs, Java Database Connectivity (JDBC), metadata repositories, file connectors, and secret stores.
• Active community: Comprehensive documentation and an active contributor community ensure robust support.

Cons

• Limited UI: Offers only a general admin GUI with basic catalog search functionalities. Advanced or specialized UIs require custom development.
• Integration setup effort: While Egeria supports diverse integrations, the configuration needs to be handled manually, unlike some competitors offering native connectors for popular tools.

Magda

Magda is a data catalog system that will give a single location for all of an organization’s data to be classified, searched, tracked, and prioritized, whether internally or externally sourced, and available as files, databases, or APIs.

Magda is a federated system, which provides a single view of all data of relevance to a user. The system can search external data sources, track changes, create automatic changes, and send out notifications when changes occur.

Pros:

• Ease of deployment: Magda supports one-click deployment across various platforms, including cloud environments, on-premises infrastructure, and local machines, using Kubernetes.
• Strong search capabilities: Offers advanced search functions using synonyms, geospatial data, user behavior analysis, and data quality metrics.
• Flexible data integration: Simplifies connecting data sources, allowing for the integration of CSV files, RDBMSs, inventory tools, RESTful APIs, and existing metadata APIs.

Cons:

• Limited visualization features: Lacks comprehensive visualization capabilities for data analysis.
• Challenges with unstructured data Handling unstructured or rapidly changing datasets is reported as more challenging, limiting its usability in certain dynamic data environments.

Key features

• Data cataloging: Data catalogs for recording individual data assets, including the data they contain and approved uses. 
• Search-based metadata management: Data asset searchability capabilities and metadata management to enhance data cataloging.
• Granular access controls: Granular access control capabilities (e.g. role-based access control) for defining and controlling access to their systems, data, and resources.
• Workflow automation: Capabilities for evaluating, charting, and automating internal flows. This can include process discovery tools, fully automated ETL functionality, and master data management.
• Data security: Features such as data quality and security capabilities, for example, usage monitoring, data lineage, and data loss prevention. 

Use cases and applications

Open-source data governance tools are versatile metadata platforms with several real-time use cases, each designed to improve organizational data management. Here are some of the main applications:

1. Metadata change notifications

These tools can be set up to send targeted notifications depending on metadata changes. For example, when a ‘PII’ tag is added to a dataset, the data governance team can receive an email alert, providing quick knowledge and action.

2. Workflow integration

By integrating open-source data governance tools with internal workflows, organizations may automate activities like creating Jira tickets when new Tags or Terms are proposed on a dataset. This will improve collaboration and tracking.

3. Synchronization for metadata changes

These tools can synchronize metadata changes with third-party systems is critical for ensuring consistency across platforms. For instance, when a tag is added or updated in a metadata management tool like DataHub, the change can automatically reflect in other environments, such as Snowflake.

4. Auditing

Auditing in data governance solutions ensures clear visibility into changes made to data, identifying who made modifications and when. This capability is essential for maintaining compliance and adhering to governance standards. By tracking these changes, organizations can:

• Enable compliance: Meet regulatory requirements by documenting a clear history of data activities.
• Maintain data integrity: Ensure all changes align with governance policies.

Further reading

External Links