Great Expectations and OpenMetadata serve fundamentally different roles in the modern data stack. Great Expectations is a focused data validation framework that lets teams write and enforce explicit data quality checks directly inside their pipelines. OpenMetadata is a broad unified metadata platform that combines data discovery, governance, lineage, observability, and quality under one roof. Teams that need deep, codified data testing with tight pipeline integration should reach for Great Expectations. Teams building an organization-wide data catalog with governance and cross-functional discovery should choose OpenMetadata. Many data-mature organizations run both tools together, using Great Expectations for granular validation and OpenMetadata as the central metadata layer.
| Feature | Great Expectations | OpenMetadata |
|---|---|---|
| Primary Focus | Data validation and quality testing | Unified metadata management, discovery, and governance |
| Pricing Model | Free and Open-Source, Paid upgrades available | Free and open-source under Apache 2.0 license |
| GitHub Stars | 11,430+ | 11,216+ |
| Core Language | Python | TypeScript (frontend) with Java backend |
| Integration Ecosystem | Airflow, Dagster, Prefect, SQL, Pandas, Spark | 100+ connectors including Snowflake, BigQuery, Kafka, Airflow, dbt |
| Deployment Complexity | Lightweight; installs via pip into existing pipelines | Four-component architecture; Docker or Kubernetes deployment |
| Metric | Great Expectations | OpenMetadata |
|---|---|---|
| GitHub stars | 11.5k | 13.8k |
| TrustRadius rating | 10.0/10 (1 reviews) | — |
| PyPI weekly downloads | 7.5M | 88.6k |
| Docker Hub pulls | — | 4.4M |
| Search interest | 0 | 1 |
As of 2026-05-04 — updated weekly.
| Feature | Great Expectations | OpenMetadata |
|---|---|---|
| Data Quality & Validation | ||
| Expectation-based data validation | Core strength with reusable Expectation Suites | Built-in data quality tests and profiling |
| Data profiling | Supported via profilers for statistical summaries | Native column-level profiling with historical trends |
| Auto-generated data documentation | Data Docs produces browsable HTML reports | Central metadata catalog with rich entity pages |
| Metadata & Discovery | ||
| Data discovery and search | Not a discovery tool; focuses on validation output | Full-text search across tables, dashboards, pipelines, and topics |
| Column-level lineage | Not supported natively | End-to-end column-level lineage tracking |
| Metadata versioning | Expectation suite versioning via Git | Built-in metadata versioning with change history |
| Governance & Collaboration | ||
| Data governance workflows | Validation results feed governance processes externally | Native governance with ownership, tags, glossaries, and policies |
| Team collaboration features | Shared Expectation Suites; GX Cloud adds collaboration UI | Conversations, tasks, and announcements on data assets |
| Role-based access control | Available in GX Cloud paid tiers | Built-in RBAC with policies and teams |
| Integration & Architecture | ||
| Pipeline orchestrator integration | Native plugins for Airflow, Dagster, Prefect | Ingestion connectors for Airflow, Dagster, Fivetran, NiFi |
| Database and warehouse connectors | SQL, Pandas, and Spark backends | 100+ connectors covering databases, dashboards, messaging, ML models |
| API-first architecture | Python API; REST API available in GX Cloud | Fully API-first with standardized schemas and OpenAPI spec |
| Operations & Observability | ||
| Data observability and monitoring | Validation checkpoints; alerting requires external setup | Native data observability with alerts and incident management |
| CI/CD integration | Validation results integrate into CI/CD pipelines directly | Metadata ingestion can be scheduled or triggered via API |
| Scalability | Scales with underlying compute (Spark, SQL engines) | Handles 2+ million data assets in large deployments |
Expectation-based data validation
Data profiling
Auto-generated data documentation
Data discovery and search
Column-level lineage
Metadata versioning
Data governance workflows
Team collaboration features
Role-based access control
Pipeline orchestrator integration
Database and warehouse connectors
API-first architecture
Data observability and monitoring
CI/CD integration
Scalability
Great Expectations and OpenMetadata serve fundamentally different roles in the modern data stack. Great Expectations is a focused data validation framework that lets teams write and enforce explicit data quality checks directly inside their pipelines. OpenMetadata is a broad unified metadata platform that combines data discovery, governance, lineage, observability, and quality under one roof. Teams that need deep, codified data testing with tight pipeline integration should reach for Great Expectations. Teams building an organization-wide data catalog with governance and cross-functional discovery should choose OpenMetadata. Many data-mature organizations run both tools together, using Great Expectations for granular validation and OpenMetadata as the central metadata layer.
Choose Great Expectations if:
Choose OpenMetadata if:
This verdict is based on general use cases. Your specific requirements, existing tech stack, and team expertise should guide your final decision.
Yes, many organizations run both tools in their data stack. Great Expectations handles granular data validation at the pipeline level, producing detailed test results. OpenMetadata can ingest those results alongside metadata from other sources, giving your team a single pane of glass for data quality status, lineage, and discovery. This combination provides deep validation from Great Expectations with the broad metadata context that OpenMetadata delivers.
OpenMetadata is the stronger choice for data governance. It provides native governance capabilities including data ownership assignment, tag-based classification, glossary management, and policy-driven access control. Great Expectations contributes to governance by ensuring data meets quality standards, but it does not offer discovery, cataloging, or access management features. Organizations focused primarily on governance workflows will get more value from OpenMetadata.
Both tools are open source under the Apache 2.0 license and free to self-host. Great Expectations offers GX Cloud as a managed service with a free Developer tier and paid Team and Enterprise plans. OpenMetadata is available as a free managed SaaS through Collate, the company founded by its creators. For teams with the infrastructure expertise to self-host, both tools cost nothing beyond compute resources.
Both projects have strong open-source communities with comparable GitHub star counts, approximately 11,400 for Great Expectations and 11,200 for OpenMetadata. Great Expectations has a mature Python ecosystem with deep roots in the data engineering community. OpenMetadata reports over 3,000 enterprise deployments, 370+ code contributors, and 11,000+ community members. The ecosystems differ in focus: Great Expectations centers on data testing, while OpenMetadata spans metadata management across the entire data stack.