ScriptAI Infrastructurev1.3

Data Quality Checker

by Great Expectations · free · Last verified 2026-03-17

Automates data quality testing for tabular data using the Great Expectations library. This script profiles datasets to generate and validate 'Expectations' covering schema, statistical properties, and referential integrity. It produces a comprehensive HTML report (Data Docs) and can be integrated into CI/CD pipelines as a quality gate to prevent bad data from entering production systems.

https://github.com/great-expectations/great_expectations ↗

B—Above Average

Adoption: B+Quality: AFreshness: ACitations: BEngagement: F

Specifications

License: Apache-2.0
Pricing: free
Capabilities: Automated data profiling and expectation suite generation, Schema validation (column names, types, order), Statistical validation (mean, median, stddev, quantiles), Value set and type checking (e.g., expect_column_values_to_be_in_set), Missing value and uniqueness checks, Distributional drift detection between dataset batches, Referential integrity checks across tables, Generation of human-readable HTML data quality reports (Data Docs), CI/CD integration for automated quality gating, Support for various backends (Pandas, Spark, SQL)
Integrations: [object Object], [object Object], [object Object], [object Object], [object Object], [object Object], [object Object]
Use Cases: [object Object], [object Object], [object Object], [object Object], [object Object]
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, validation, profiling, data-testing, ci-cd, automation, data-governance, etl-testing, data-observability, schema-validation
Added: 2026-03-17
Completeness: 0.8%

Index Score

Adoption

Quality

Freshness

Citations

Engagement

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