OpenMetadata vs Databricks
Side-by-side comparison of AI visibility scores, market position, and capabilities
OpenMetadata is an open-source metadata management and data catalog platform providing discovery, governance, lineage, and data quality across the modern data stack.
About
OpenMetadata is an open-source metadata management platform that provides data catalog, data discovery, data lineage, data quality, and data governance capabilities through a single unified metadata store, designed to serve as the central metadata layer for the modern data stack across cloud data warehouses, ETL pipelines, BI tools, ML platforms, and operational databases. The platform's architecture is built on a metadata API layer that aggregates metadata from connected data sources into a centralized repository with a standardized schema, enabling search and discovery across the full data environment without requiring each tool to be queried separately. OpenMetadata's open-source foundation means that organizations can deploy the platform without vendor licensing costs and can extend or customize the platform for specific use cases by contributing to or forking the codebase — an important consideration for data engineering teams that need to adapt catalog functionality to match their specific data stack configuration.
$4.8B revenue run-rate; 55% YoY growth; $134B valuation (Series L). Mosaic AI for enterprise LLM fine-tuning and inference; Unity Catalog for data governance. DBRX open-source model; every major enterprise AI deployment runs on the lakehouse.
About
Databricks was founded in 2013 by the original creators of Apache Spark — Ali Ghodsi, Matei Zaharia, and five other UC Berkeley researchers — to unify data engineering, analytics, and machine learning on a single platform. The company commercialized the lakehouse architecture, combining the flexibility of data lakes with the reliability of data warehouses. Databricks runs on AWS, Azure, and GCP and leads the commercial distribution of the open-source Delta Lake and MLflow projects.\n\nThe platform includes the Databricks Lakehouse for unified data processing, Unity Catalog for governance and lineage tracking, and Mosaic AI for enterprise LLM fine-tuning, model serving, and generative AI application development. It supports data engineering, SQL analytics, BI, feature engineering, and model training within a single governance perimeter, serving enterprises in financial services, healthcare, manufacturing, and media.\n\nDatabricks achieved a $4.8 billion annualized revenue run-rate in early 2025 with 55% year-over-year growth and a $62 billion valuation from its Series L round — one of the most valuable private software companies globally. Its dual role as the leading commercial lakehouse vendor and steward of influential open-source projects gives it a unique ecosystem advantage as enterprises accelerate investment in AI infrastructure.
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