Decision comparison
Great Expectations vs OpenMetadata
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.
Used together. These are normally used together rather than chosen between. The comparison explains what each one does in the stack.
These are different kinds of product — Data Validation Framework and Data Catalog.
Quick Comparison
| Decision factor | 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+ | 15,121 |
| Core Language | Python | TypeScript (frontend) with Java backend |
| Integration Ecosystem | Airflow, Dagster, Prefect, SQL, Pandas, Spark | 120+ native connectors including Snowflake, BigQuery, Kafka, Airflow, dbt |
| Deployment Complexity | Lightweight; installs via pip into existing pipelines | Four-component architecture; Docker or Kubernetes deployment |
Great Expectations
- Primary Focus:
- Data validation and quality testing
- Pricing Model:
- Free and Open-Source, Paid upgrades available
- GitHub Stars:
- 11,430+
- Core Language:
- Python
- Integration Ecosystem:
- Airflow, Dagster, Prefect, SQL, Pandas, Spark
- Deployment Complexity:
- Lightweight; installs via pip into existing pipelines
OpenMetadata
- Primary Focus:
- Unified metadata management, discovery, and governance
- Pricing Model:
- Free and open-source under Apache 2.0 license
- GitHub Stars:
- 15,121
- Core Language:
- TypeScript (frontend) with Java backend
- Integration Ecosystem:
- 120+ native connectors including Snowflake, BigQuery, Kafka, Airflow, dbt
- Deployment Complexity:
- Four-component architecture; Docker or Kubernetes deployment
Public signals
Verified factual signals only. Bars appear only for like-for-like metrics with five weekly assessments for every tool; missing evidence stays explicit. These signals do not establish enterprise adoption, product quality, or total cost.
| Metric | Great Expectations | OpenMetadata |
|---|---|---|
| GitHub commits, 90d(Product adoption) | 181 | 2.3k |
| GitHub stars(Product adoption) | 11,000+ | 15,000+ |
| Search interest(Market interest) | 0 | 1 |
| Hacker News mentions, 90d(Community interest) | 0 | Not available |
| PyPI weekly downloads(Product adoption) | 4.5M | 41.4k |
| Stack Overflow questions(Community interest) | 148 | Not available |
| Docker Hub pulls(Product adoption) | Not available | 5.4M |
As of October 12, 2026 — updated weekly.
Health & risk evidence
Observed public-source checks for mapped package versions and repositories.
Great Expectations
October 12, 2026Package vulnerabilities
PyPI · great-expectations@1.24.0
0 vulnerabilities
across 1 package
Repository security score
Not available
OpenMetadata
October 12, 2026Package vulnerabilities
PyPI · openmetadata-ingestion@2.0.5.0
0 vulnerabilities
across 1 package
Repository security score
github.com/open-metadata/OpenMetadata
4.9/10
Feature Comparison
| 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 | 120+ native 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 |
Data Quality & Validation
Expectation-based data validation
Data profiling
Auto-generated data documentation
Metadata & Discovery
Data discovery and search
Column-level lineage
Metadata versioning
Governance & Collaboration
Data governance workflows
Team collaboration features
Role-based access control
Integration & Architecture
Pipeline orchestrator integration
Database and warehouse connectors
API-first architecture
Operations & Observability
Data observability and monitoring
CI/CD integration
Scalability
How they fit together
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.
What each one handles
Use Great Expectations for:
We recommend Great Expectations for data engineering teams that need fine-grained, codified data quality checks embedded directly in their ETL and ELT pipelines. Use it when the primary goal is to catch data issues at the source before they propagate downstream. Teams already working in Python-centric stacks with orchestrators like Airflow, Dagster, or Prefect will find GX integrates seamlessly into their existing workflows with minimal overhead.
Use OpenMetadata for:
We recommend OpenMetadata for organizations that need a centralized metadata platform to unify data discovery, governance, lineage, and observability across their entire data estate. Use it when analysts, data scientists, and business users need to find, understand, and trust data assets without digging through code. With 120+ native connectors and built-in collaboration features, OpenMetadata excels at breaking down data silos across large, heterogeneous environments.
These roles reflect the available product evidence. Most teams run both; which one owns a given job depends on your stack and team.
Frequently Asked Questions
Can Great Expectations and OpenMetadata be used together?
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.
Which tool is better for data governance?
OpenMetadata includes data-governance capabilities. 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.
How do the pricing models compare for Great Expectations and 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.
How do the communities and ecosystems differ?
Both projects maintain public open-source communities; check their repositories for current activity and release information. Great Expectations has a mature Python ecosystem with deep roots in the data engineering community. OpenMetadata reports over 4,000 enterprise deployments, 450+ code contributors, and 13,500+ open-source community members. The ecosystems differ in focus: Great Expectations centers on data testing, while OpenMetadata spans metadata management across the entire data stack.