Marquez vs DataHub
Side-by-side — pricing, strengths, tradeoffs, and fit.
Marquez
Metadata collection and visualization for data ecosystems.
DataHub
LinkedIn's metadata search & discovery tool for data infrastructure.
At a glance
Open SourceFree Tier
● Established·From $0
Open SourceFree Tier
● Established·From $0
Overview
One-liner
One-liner
Metadata collection and visualization for data ecosystems.
One-liner
LinkedIn's metadata search & discovery tool for data infrastructure.
Maturity
Maturity
EstablishedMaturity
EstablishedOpen source
Open source
✓ YesOpen source
✓ YesPricing
Free tier
Free tier
✓ AvailableOpen source — free to use
Free tier
✓ AvailableOpen source — free to use
Starts at
Starts at
$0
Starts at
$0
Model
Model
Flat rate
Model
Flat rate
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 needing comprehensive metadata management across multiple systems.
Teams looking for a unified view of their data assets to improve collaboration and governance.
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 or startups with limited data sources that do not require centralized metadata management.
Projects requiring real-time data processing where DataHub's self-hosted model may introduce latency.
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
Centralized metadata management across multiple systems.
Search and discovery of data assets within an organization.
Integration with various data sources for comprehensive metadata collection.
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
Steep learning curve for non-Java developers
Limited documentation and community support
Complex setup and configuration process
Performance issues at scale