Data Quality Checker
by Great Expectations · open-source · Last verified 2026-03-17
Runs automated data quality checks on tabular datasets using Great Expectations, producing a profiling report with schema validation, distribution drift alerts, and referential integrity checks. Integrates with CI/CD pipelines to block model training when data quality gates fail.
https://github.com/great-expectations/great_expectations ↗B
B—Above Average
Adoption: B+Quality: AFreshness: ACitations: BEngagement: F
Specifications
- License
- Apache-2.0
- Pricing
- open-source
- Capabilities
- schema-validation, distribution-drift, referential-integrity, ci-cd-gate
- Integrations
- great-expectations, pandas, sqlalchemy, slack, github-actions
- Use Cases
- ml-training-data-validation, etl-qa, analytics-data-trust
- API Available
- No
- Language
- python
- Dependencies
- great-expectations, pandas, sqlalchemy, slack-sdk
- Environment
- Python 3.10+
- Est. Runtime
- 2-10 minutes per dataset
- Tags
- data-quality, great-expectations, deequ, validation, profiling
- Added
- 2026-03-17
- Completeness
- 100%
Index Score
62Adoption
75
Quality
85
Freshness
84
Citations
60
Engagement
0