Great Expectations
Data quality framework for defining, testing, and documenting expectations about your data pipelines.
Great Expectations
Great Expectations (GX) is the leading open-source Python library for data quality. It lets you define "expectations" — assertions about your data — and automatically generates human-readable documentation and data quality reports.
Key Features
- Expectation Suite: 300+ built-in expectations (column types, ranges, uniqueness, regex, statistical distributions)
- Data Docs: Auto-generated HTML reports showing expectation results with data samples
- Checkpoints: Integrate validation into Airflow, Prefect, Dagster, dbt, or any CI/CD pipeline
- Multi-backend: Validate data in Pandas, Spark, Snowflake, BigQuery, Redshift, Databricks
- Custom expectations: Extend with Python for domain-specific rules
- Profiling: Auto-generate an initial Expectation Suite from a data sample
Quick Start
pip install great_expectations
gx init
import great_expectations as gx
context = gx.get_context()
batch = context.sources.pandas_default.read_csv("my_data.csv")
batch.expect_column_values_to_not_be_null("user_id")
batch.expect_column_values_to_be_between("age", min_value=0, max_value=120)
batch.expect_column_values_to_match_regex("email", r".+@.+\..+")
results = batch.validate()
print(results.success)
Add to ai-supply
npx ai-supply add great-expectations-data-quality
Curated mirror of the open-source Great Expectations (Apache-2.0). Get it from the source.
Compromise signals — malicious or tampered code (leaked secrets, backdoors, a dropped executable) — reduce the score, and known dependency CVEs carry a bounded penalty (they warrant review but never QUARANTINE — update the dependency to clear). Other dangerous-by-capability traits are risk surface, expected for some capabilities. Every finding is mapped to its OWASP control below.
Findings mapped to the OWASP Top 10 for LLM Applications (2025) and the OWASP Machine Learning Security Top 10. Expand any flagged control for the exact findings — compromise reduces the score; expected/risk-surface do not, except a known CVE, which carries a small bounded penalty (high/critical → Review).
The same gate an agent runs before installing (POST /api/v1/trust/great-expectations-data-quality/check). Click a policy:
Consume Great Expectations programmatically. Authenticate with an API key or session — see Authorize an agent.
# Agents: CHECK BEFORE YOU INSTALL (no auth) — score, grade, level, capability manifest
curl https://ai-supply.store/api/v1/trust/great-expectations-data-quality
# Gate against your org policy (returns { pass, violations })
curl -X POST https://ai-supply.store/api/v1/trust/great-expectations-data-quality/check \
-H "Content-Type: application/json" \
-d '{"minGrade":"B","denyPermissions":["shell"],"denyUnknownEgress":true}'
# CLI
npx ai-supply add great-expectations-data-quality
# REST (install → download)
curl -X POST https://ai-supply.store/api/v1/listings/great-expectations-data-quality/install \
-H "Authorization: Bearer $AIM_KEY"
# MCP tool
install_listing({ "slug": "great-expectations-data-quality" })OpenAPI spec →Curated mirror — latest upstream source. See the repository for tagged releases.