Marquez vs Amundsen
Side-by-side — pricing, strengths, tradeoffs, and fit.
Marquez
Metadata collection and visualization for data ecosystems.
Amundsen
Open-source metadata-driven data discovery and catalog tool.
At a glance
Open SourceFree Tier
● Established·From $0
Open SourceFree Tier
◌ Declining·From N/A
Overview
One-liner
One-liner
Metadata collection and visualization for data ecosystems.
One-liner
Open-source metadata-driven data discovery and catalog tool.
Maturity
Maturity
EstablishedMaturity
DecliningOpen source
Open source
✓ YesOpen source
✓ YesPricing
Free tier
Free tier
✓ AvailableOpen source — free to use
Free tier
✓ AvailableStarts at
Starts at
$0
Starts at
N/A
Model
Model
Flat rate
Model
N/A
Enterprise
Enterprise
✕ NoneEnterprise
✕ NoneBest for
✓ Best for
Teams building or maintaining large-scale data pipelines who need to understand data lineage and dependencies.
Organizations implementing MLOps practices that require comprehensive metadata management.
✓ Best for
Organizations with diverse data sourcesAmundsen's integration capabilities allow for a unified metadata view.
Teams looking for an open-source solutionProvides flexibility and community support for customization.
Not ideal for
✕ Not for
Small projects with simple data flows where manual tracking is sufficient.
Real-time streaming applications requiring low-latency metadata processing.
✕ Not for
Small teams with limited technical expertiseThe setup and configuration can be complex and require technical skills.
Organizations needing advanced UI featuresThe tool focuses more on backend capabilities than on user interface.
Strengths
↑ Strengths
Metadata collection and aggregation from various data sources.
Visualization of data lineage and dependencies.
Integration with popular data platforms like Apache Airflow, Kafka, and Spark.
↑ Strengths
Open-source and community-driven
Integration with multiple data sources
Active development and support
Weaknesses
↓ Weaknesses
Steep learning curve for non-Java developers
Limited documentation and community support
Integration complexity with non-supported data platforms
Performance issues at scale
↓ Weaknesses
Complex setup and configuration
Limited user interface features
Scalability concerns for very large datasets