Marquez vs OpenDataDiscovery Platform
Marquez
marquezproject.aiOpenDataDiscovery Platform
opendatadiscovery.orgSide by side
| Figure | Marquez | OpenDataDiscovery Platform |
|---|---|---|
| Price from | — | Free |
| Free plan | — | Yes |
| Free trial | — | Yes |
| Open source | — | — |
| Web | Yes | Yes |
| Windows | Yes | — |
| macOS | Yes | — |
| Linux | Yes | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 0 | 1 |
| Spec score · Active Metadata Management Software | 7.2 (#15) | 8.1 (#7) |
| Spec score · Data Lineage Tools | 7.1 (#27) | 8.1 (#9) |
| Audience | Marquez is designed for data teams managing metadata and lineage across data processing applications and pipelines. | The platform targets data teams of any size, including enterprises, small and midsize companies, and data science teams. |
| Deployment | The deployment guide describes Docker and Kubernetes deployments and says the HTTP API server uses PostgreSQL to store dataset, job, and run metadata. | The site offers local Docker Compose, Kubernetes deployment with Helm charts, and AWS Marketplace QuickLaunch. |
| Integrations | The site names Apache Airflow, Apache Spark, Apache Flink, dbt, and Dagster as integrations supported through the OpenLineage community. | The product site lists integrations including MongoDB, ClickHouse, dbt, Snowflake, Tableau, BigQuery, Kafka, PostgreSQL, and others. |
| Purpose | Marquez consumes, stores, and visualizes OpenLineage metadata for use cases including data governance, data quality monitoring, and performance analytics. | OpenDataDiscovery describes itself as an end-to-end data discovery, observability, and governance platform. |
| Support | The site directs users to its Slack channel for help and feedback and encourages newcomers to join monthly community meetings. | The product site lists a dedicated team, a helpful team, and a community as support options. |
Plans
In their own words
OpenDataDiscovery Platform
OpenDataDiscovery Platform is an open-source catalog for finding and governing data products across data teams. Search and facet filters help users locate metadata by source, owner, or tag, then explore ownership and related links. The platform traces data objects and microservices end to end, imports data quality test results, and shows dataset quality status. It also catalogs machine-learning experiment metadata and lineage and supports experiment logging, connecting data science and data engineering work. Teams can access collected metadata through a web interface or REST API, and deploy with Docker Compose, Kubernetes and Helm charts, or AWS Marketplace QuickLaunch. The catalog uses an open metadata collection standard to connect catalogs, tools, and feature stores; Lookup Tables support reference data governance. ODD supports LDAP authentication and USER and ADMIN roles. Its documentation cautions against simple login-form authentication in production. Metadata attachments are limited to 20 MB, while links can point to larger or remotely stored files. The platform is free and open source, with public development.