Decision comparison
Collibra vs Great Expectations
Collibra and Great Expectations serve fundamentally different functions in the data ecosystem. Collibra is a comprehensive enterprise governance platform that unifies data catalog, lineage, quality monitoring, privacy management, AI governance, and compliance reporting into a single product trusted by Fortune 500 companies. Great Expectations is a focused, open-source data validation framework that gives data engineers precise, code-level control over data quality checks within their pipelines. The two tools do not directly compete — they address different layers of the data quality and governance stack and are frequently used together in enterprise environments.
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 Catalog and Data Validation Framework.
Quick Comparison
| Decision factor | Collibra | Great Expectations |
|---|---|---|
| Best For | Enterprises needing unified data and AI governance with catalog, lineage, privacy, and compliance in one platform | Data engineers who want code-first, explicit data validation embedded directly in their pipelines |
| Pricing Model | Contact for pricing | Free and Open-Source, Paid upgrades available |
| Deployment | Cloud-based SaaS platform designed for regulated industries | Self-hosted (GX Core) or SaaS (GX Cloud) |
| Data Quality Approach | Observability-driven quality monitoring with automated remediation and data contract enforcement | Expectation-based validation with codified rules, auto-generated documentation, and ExpectAI test generation |
| Governance Scope | Full-spectrum governance covering data catalog, lineage, privacy, AI registry, data marketplace, and compliance reporting | Focused on data validation and quality testing — no catalog, lineage, or compliance features |
| Learning Curve | Moderate to steep — enterprise platform with workflow designer, semantic graph, and federated governance model | Moderate — requires Python proficiency and manual expectation definition |
Collibra
- Best For:
- Enterprises needing unified data and AI governance with catalog, lineage, privacy, and compliance in one platform
- Pricing Model:
- Contact for pricing
- Deployment:
- Cloud-based SaaS platform designed for regulated industries
- Data Quality Approach:
- Observability-driven quality monitoring with automated remediation and data contract enforcement
- Governance Scope:
- Full-spectrum governance covering data catalog, lineage, privacy, AI registry, data marketplace, and compliance reporting
- Learning Curve:
- Moderate to steep — enterprise platform with workflow designer, semantic graph, and federated governance model
Great Expectations
- Best For:
- Data engineers who want code-first, explicit data validation embedded directly in their pipelines
- Pricing Model:
- Free and Open-Source, Paid upgrades available
- Deployment:
- Self-hosted (GX Core) or SaaS (GX Cloud)
- Data Quality Approach:
- Expectation-based validation with codified rules, auto-generated documentation, and ExpectAI test generation
- Governance Scope:
- Focused on data validation and quality testing — no catalog, lineage, or compliance features
- Learning Curve:
- Moderate — requires Python proficiency and manual expectation definition
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 | Collibra | Great Expectations |
|---|---|---|
| GitHub commits, 90d(Developer adoption) | 107 | Not available |
| GitHub stars(Developer adoption) | 36 | Not available |
| Search interest(Market interest) | 0 | 0 |
| Hacker News mentions, 90d(Community interest) | 0 | 0 |
| Stack Overflow questions(Community interest) | 10 | 148 |
| GitHub commits, 90d(Product adoption) | Not available | 169 |
| GitHub stars(Product adoption) | Not available | 11,000+ |
| PyPI weekly downloads(Product adoption) | Not available | 4.5M |
As of September 21, 2026 — updated weekly.
Health & risk evidence
Observed public-source checks for mapped package versions and repositories.
Collibra
Package vulnerabilities
Not available
Repository security score
Not available
Great Expectations
September 21, 2026Package vulnerabilities
PyPI · great-expectations@1.23.1
0 vulnerabilities
across 1 package
Repository security score
Not available
Feature Comparison
| Feature | Collibra | Great Expectations |
|---|---|---|
| Data Quality | ||
| Quality Monitoring | Dedicated Data Quality & Observability product with automated anomaly detection and data remediation workflows | Expectation Suites define explicit validation rules that run against datasets in SQL, Pandas, or Spark backends |
| Data Profiling | Built-in data profiling integrated with the governance platform to surface quality metrics alongside business context | Basic profiling through expectation results and Data Docs; no standalone profiling engine |
| Data Contracts | Native data contracts feature (GA April 2026) promoting alignment across teams with enhanced visibility and automation | No formal data contracts; expectations serve as informal contracts between data producers and consumers |
| Governance & Catalog | ||
| Data Catalog | Full-featured data catalog for end-to-end visibility, data discovery, and business context across the enterprise | Not available — focused exclusively on data validation |
| Data Lineage | Automated cross-platform lineage mapping relationships between systems, applications, and reports enterprise-wide | Not available — relies on external catalog or lineage tools |
| AI Governance | Unified AI registry for cataloging, assessing, and monitoring AI use cases, models, and agents with automated traceability across Vertex AI, SageMaker, and Databricks | Not available — no AI model or use case governance capabilities |
| Integration & Extensibility | ||
| Platform Integrations | 100+ native integrations including Salesforce, Databricks, Tableau, Slack, Snowflake, AWS, and Google via the Collibra Everywhere browser extension | Native integrations with Airflow, Dagster, and Prefect orchestrators plus SQL, Pandas, and Spark backends |
| Open Source | Proprietary commercial platform with no open-source components | Fully open source under Apache-2.0 license with 11,000+ GitHub stars and an active contributor community |
| API & Extensibility | REST APIs, partner integrations, and developer portal for custom extensions and workflows | Python-native extensibility — custom expectations, data sources, and plugins with full framework access |
| Collaboration & Workflow | ||
| Workflow Automation | Intuitive workflow designer for automating governance processes, approvals, and cross-team collaboration | No workflow automation — validation results must be consumed by external orchestration tools |
| Documentation | Business glossary, data notebook assets with collaborative SQL queries, and semantic graph connecting business terms to physical data | Auto-generated Data Docs providing human-readable documentation of every validation check and result |
| Data Marketplace | Built-in Data Marketplace for discovering, understanding, and accessing trusted data products across the organization | Not available — no data product discovery or sharing capabilities |
| Security & Compliance | ||
| Privacy & Compliance | Dedicated Data Privacy product with automated risk reporting, GDPR workflow support, and centralized compliance management | No built-in privacy or compliance features |
| Access Control | Enterprise-grade security with federated governance model, role-based access, and compliance certifications for regulated industries | Basic access control via GX Cloud; no built-in RBAC in the open-source GX Core framework |
| Audit Trail | Full audit trail with integrated compliance reporting and automated risk assessments across data sources | Validation results are logged and documented in Data Docs but no formal governance audit trail |
Data Quality
Quality Monitoring
Data Profiling
Data Contracts
Governance & Catalog
Data Catalog
Data Lineage
AI Governance
Integration & Extensibility
Platform Integrations
Open Source
API & Extensibility
Collaboration & Workflow
Workflow Automation
Documentation
Data Marketplace
Security & Compliance
Privacy & Compliance
Access Control
Audit Trail
How they fit together
Collibra and Great Expectations serve fundamentally different functions in the data ecosystem. Collibra is a comprehensive enterprise governance platform that unifies data catalog, lineage, quality monitoring, privacy management, AI governance, and compliance reporting into a single product trusted by Fortune 500 companies. Great Expectations is a focused, open-source data validation framework that gives data engineers precise, code-level control over data quality checks within their pipelines. The two tools do not directly compete — they address different layers of the data quality and governance stack and are frequently used together in enterprise environments.
What each one handles
Use Collibra for:
Choose Collibra if your organization needs a unified governance platform that covers data catalog, lineage, quality monitoring, privacy management, AI governance, and compliance reporting. Collibra is built for regulated enterprises managing complex data ecosystems across multiple departments, and it provides the collaboration workflows, semantic graph, and business context layer needed to scale data governance organization-wide.
Use Great Expectations for:
Choose Great Expectations if you are a data engineer or data team that needs free, open-source, code-first data validation embedded directly in your pipelines. Great Expectations gives you full control over expectation definitions, native integration with orchestrators like Airflow and Dagster, and auto-generated Data Docs that document every quality check — all with zero licensing cost and no vendor lock-in.
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 replace Collibra for data governance?
No. Great Expectations is a data validation framework, not a data governance platform. It handles one specific piece of the governance puzzle — verifying that data meets defined quality expectations. Collibra provides the broader governance layer including data catalog, lineage, privacy management, AI governance, compliance reporting, and cross-team collaboration workflows. Organizations that need full governance capabilities require a platform like Collibra, while Great Expectations addresses the validation layer within data pipelines.
Is Great Expectations free to use?
Yes. GX Core is fully open source under the Apache-2.0 license and free to download, deploy, and extend with no usage limits. Great Expectations also offers GX Cloud, a managed platform with a free Developer tier and paid Team and Enterprise tiers for teams that want hosted infrastructure, collaboration features, and a UI without self-hosting overhead.
How much does Collibra cost?
Collibra uses an enterprise pricing model that requires contacting their sales team for a quote. Pricing is not published publicly. Collibra is designed for large organizations in regulated industries — customers include Fortune 500 companies across financial services, healthcare, life sciences, and retail. The platform is recognized as a Leader in Gartner's Magic Quadrant for Data and Analytics Governance Platforms.
Can I use Collibra and Great Expectations together?
Yes, and this is a common pattern for enterprise data teams. Great Expectations handles in-pipeline data validation, running codified expectation checks against datasets as part of orchestrated workflows in Airflow, Dagster, or Prefect. Collibra provides the governance layer on top, cataloging data assets, tracking lineage, managing compliance, and giving business users a way to discover and trust data. The two tools address different layers of the data quality and governance stack and complement each other well.
Which tool is better for small data teams?
Great Expectations is the better fit for smaller teams. It is free, Python-native, and integrates directly into existing data pipelines without requiring a separate platform or enterprise sales engagement. GX Cloud also offers a free Developer tier for teams that want a hosted UI. Collibra is built for enterprise-scale organizations with complex governance needs across multiple departments, and its pricing and feature set reflect that positioning.