Great Expectations vs Secoda
Great Expectations and Secoda solve fundamentally different problems in the modern data stack. Great Expectations is a specialized data validation framework that excels at pipeline-level quality testing, while Secoda is a broad data enablement platform focused on discovery, cataloging, and AI-powered governance. Most teams will not choose one over the other — they complement each other well. However, if you must pick one, your decision hinges on whether your primary pain point is data correctness at the pipeline level or data discoverability and governance across your organization.
Great Expectations4.6Secoda4.6
Data Quality
Quick Comparison
| Feature | Great Expectations | Secoda |
|---|---|---|
| Primary Focus | Data validation and quality testing | Data discovery, cataloging, and AI-powered governance |
| Pricing Model | Free and Open-Source, Paid upgrades available | Free tier with 1 editor, 500 resources, 2 integrations; Premium starts at $99/month, Enterprise contact for pricing |
| Deployment | Self-hosted or GX Cloud | Cloud-hosted, self-hosted available on Enterprise |
| Best For | Data engineers who need pipeline-level validation | Teams needing a unified data catalog with AI search and governance |
| Learning Curve | Moderate — requires Python knowledge and test authoring | Low — browser-based UI with AI-assisted search and documentation |
| Integration Depth | Deep pipeline integration with Airflow, Dagster, Prefect | Broad stack integration for metadata, lineage, and monitoring |
Great Expectations
- Primary Focus:
- Data validation and quality testing
- Pricing Model:
- Free and Open-Source, Paid upgrades available
- Deployment:
- Self-hosted or GX Cloud
- Best For:
- Data engineers who need pipeline-level validation
- Learning Curve:
- Moderate — requires Python knowledge and test authoring
- Integration Depth:
- Deep pipeline integration with Airflow, Dagster, Prefect
Secoda
- Primary Focus:
- Data discovery, cataloging, and AI-powered governance
- Pricing Model:
- Free tier with 1 editor, 500 resources, 2 integrations; Premium starts at $99/month, Enterprise contact for pricing
- Deployment:
- Cloud-hosted, self-hosted available on Enterprise
- Best For:
- Teams needing a unified data catalog with AI search and governance
- Learning Curve:
- Low — browser-based UI with AI-assisted search and documentation
- Integration Depth:
- Broad stack integration for metadata, lineage, and monitoring
Interface Preview
Secoda
Feature Comparison
| Feature | Great Expectations | Secoda |
|---|---|---|
| Data Quality & Validation | ||
| Expectation-based data testing | Core strength — define, execute, and reuse expectation suites | Data Quality Score for monitoring but no custom test authoring |
| Automated anomaly detection | Alert-based validation failures in pipelines | Real-time monitoring and anomaly detection across the data stack |
| Data profiling | Built-in profilers for automatic expectation generation | Metadata enrichment and quality scoring |
| Data Cataloging & Discovery | ||
| Data catalog | Not included — focused on validation only | Full data catalog with search, tagging, and organization |
| AI-powered search | ❌ | AI search across entire data landscape with natural language queries |
| Data lineage | Not included natively | End-to-end generated lineage from source to dashboard |
| Governance & Compliance | ||
| Access control (RBAC) | Managed at infrastructure level, not built in | Built-in RBAC, SAML, SSO, and access request management |
| PII scanning | Not included | Available on Premium tier with automated identification |
| Policy enforcement | Enforced via expectation suites in CI/CD pipelines | Dedicated policy engine with automated rules and real-time alerts |
| Documentation & Collaboration | ||
| Auto-generated documentation | Data Docs — auto-generated HTML reports of validation results | AI Documentation Agent generates descriptions for all data assets |
| Data dictionary | Not included | Built-in data dictionary with searchable definitions |
| Knowledge repository | Not included | Searchable Q&A repository eliminates repetitive data requests |
| Developer & Platform | ||
| Open-source core | Yes — Apache-2.0 license, 11,400+ GitHub stars | No — proprietary SaaS platform |
| API access | Python API for programmatic test creation and execution | REST API available on all paid tiers |
| AI agents and automation | ExpectAI for auto-generating tests from data | Nine specialized AI agents for analysis, search, governance, and more |
Data Quality & Validation
Expectation-based data testing
Great ExpectationsCore strength — define, execute, and reuse expectation suites
SecodaData Quality Score for monitoring but no custom test authoring
Automated anomaly detection
Great ExpectationsAlert-based validation failures in pipelines
SecodaReal-time monitoring and anomaly detection across the data stack
Data profiling
Great ExpectationsBuilt-in profilers for automatic expectation generation
SecodaMetadata enrichment and quality scoring
Data Cataloging & Discovery
Data catalog
Great ExpectationsNot included — focused on validation only
SecodaFull data catalog with search, tagging, and organization
AI-powered search
Great Expectations❌
SecodaAI search across entire data landscape with natural language queries
Data lineage
Great ExpectationsNot included natively
SecodaEnd-to-end generated lineage from source to dashboard
Governance & Compliance
Access control (RBAC)
Great ExpectationsManaged at infrastructure level, not built in
SecodaBuilt-in RBAC, SAML, SSO, and access request management
PII scanning
Great ExpectationsNot included
SecodaAvailable on Premium tier with automated identification
Policy enforcement
Great ExpectationsEnforced via expectation suites in CI/CD pipelines
SecodaDedicated policy engine with automated rules and real-time alerts
Documentation & Collaboration
Auto-generated documentation
Great ExpectationsData Docs — auto-generated HTML reports of validation results
SecodaAI Documentation Agent generates descriptions for all data assets
Data dictionary
Great ExpectationsNot included
SecodaBuilt-in data dictionary with searchable definitions
Knowledge repository
Great ExpectationsNot included
SecodaSearchable Q&A repository eliminates repetitive data requests
Developer & Platform
Open-source core
Great ExpectationsYes — Apache-2.0 license, 11,400+ GitHub stars
SecodaNo — proprietary SaaS platform
API access
Great ExpectationsPython API for programmatic test creation and execution
SecodaREST API available on all paid tiers
AI agents and automation
Great ExpectationsExpectAI for auto-generating tests from data
SecodaNine specialized AI agents for analysis, search, governance, and more
✅ Full support⚠️ Partial / limited❌ Not supported
Our Verdict
Great Expectations and Secoda solve fundamentally different problems in the modern data stack. Great Expectations is a specialized data validation framework that excels at pipeline-level quality testing, while Secoda is a broad data enablement platform focused on discovery, cataloging, and AI-powered governance. Most teams will not choose one over the other — they complement each other well. However, if you must pick one, your decision hinges on whether your primary pain point is data correctness at the pipeline level or data discoverability and governance across your organization.
When to Choose Each
Choose Great Expectations if:
Choose Secoda if:
This verdict is based on general use cases. Your specific requirements, existing tech stack, and team expertise should guide your final decision.
Frequently Asked Questions
Can Great Expectations and Secoda be used together?
Yes, and many data teams do exactly that. Great Expectations handles the pipeline-level data validation — catching schema drift, null violations, and statistical anomalies before data reaches your warehouse. Secoda then serves as the discovery and governance layer on top, cataloging validated data assets, tracking lineage, and making everything searchable for the broader organization. The two tools address different layers of the data quality problem and complement each other well.
Which tool is better for a small data team just getting started?
It depends on your most pressing need. If your pipelines are breaking and you need immediate data validation, Great Expectations is free to start with and integrates directly into your existing Python workflows. If your team struggles more with finding and understanding existing data assets, Secoda offers a free tier with one editor, 500 resources, and two integrations, which is enough to evaluate its cataloging and search capabilities.
Does Great Expectations offer a managed cloud service?
Yes. GX Cloud is the managed offering from the Great Expectations team. It provides a hosted environment for running validations, managing expectation suites, and viewing Data Docs without maintaining your own infrastructure. GX Cloud includes collaboration features and observability tools beyond what the open-source GX Core framework provides on its own.
How does Secoda handle data quality compared to Great Expectations?
Secoda approaches data quality from a monitoring and scoring perspective rather than a testing perspective. It provides a Data Quality Score that gives an instant view of data health, along with real-time monitoring and anomaly detection. However, it does not offer the granular, code-defined expectation suites that Great Expectations provides. For teams that need both deep validation logic and broad observability, combining the two tools covers both angles.