OpenMetadata has emerged as a notable open-source option for data teams building metadata management infrastructure. The platform offers data discovery, end-to-end lineage tracking (including column-level), business glossaries, data quality testing, and connectors for 50+ data sources spanning warehouses, BI tools, and pipelines.
The architecture uses a relational metadata model with event-based updates and graph storage, emphasizing usability for analysts while remaining extensible via APIs. It competes directly with DataHub in the open-source space and positions itself against commercial platforms like Collibra, Alation, and Atlan.
What sets OpenMetadata apart is governance built into the core rather than bolted on. Ownership, tags, certifications, and data quality signals tie directly to assets. The platform integrates with Great Expectations and Prefect for data quality workflows.
The trade-offs are real. Teams choosing OpenMetadata need engineering capacity for deployment (Helm charts for Kubernetes), connector troubleshooting (common issues with Snowflake, Postgres, and Oracle integrations), and Airflow DAG configuration for ingestion pipelines. The streaming architecture scales but assumes strong operational support.
For organizations already running modern data stacks with engineering teams comfortable managing open-source infrastructure, OpenMetadata offers flexibility and avoids vendor lock-in. For teams needing faster time-to-value or lacking dedicated platform engineers, managed alternatives make more sense.
The GitHub repository shows active community contributions across industries, though no major releases appeared in early 2026. The project remains one of the fastest-growing open-source data tools, but that growth doesn't eliminate the operational complexity.
Three things to watch: connector stability in production (authentication timeouts remain a reported issue), Airflow version compatibility as both projects evolve, and how the project handles enterprise feature requests without compromising the open-source model. The real question for CTOs is whether the total cost of ownership beats commercial platforms once engineering time factors in.