# fivetran/great_expectations

Always know what to expect from your data.

Repository: https://github.com/fivetran/great_expectations
Canonical: https://ross.abutalabs.com/products/great_expectations
Homepage: https://docs.greatexpectations.io/
Language: Python
License: Apache-2.0
License Family: permissive
Topics: pipeline-tests, dataquality, datacleaning, datacleaner, data-science, data-profiling, pipeline, pipeline-testing, cleandata, dataunittest, data-unit-tests, eda, exploratory-data-analysis, exploratory-analysis, exploratorydataanalysis, data-quality, data-engineering, pipeline-debt, data-profilers, mlops
Last push: 2026-08-26T14:53:00+00:00

## Health v2 (maintenance only)
Score: 95/100 (v2, computed 2026-09-03T02:39:23.370411+00:00)
- activity 99, release rhythm 86, longevity 100
- inputs: {"age_days": 3279, "days_push": 7, "days_rel": 14, "gap_med": 7, "n_releases_24m": 90}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 11740, forks 1813 (observed 2026-08-28T04:10:49.620829+00:00)

## What it is
Great Expectations (GX Core) is a Python library for validating, documenting, and profiling data using declarative, human-readable assertions called Expectations, which act as unit tests for your data. It generates data quality reports and documentation from validation results and integrates with common data sources like Snowflake, BigQuery, Spark, and Pandas.

## Use cases
- validate data quality in etl pipelines
- write unit tests for my data
- profile datasets and generate data documentation
- catch bad data from vendors before loading
- assert column values fall within expected ranges
- add data quality checks to airflow pipelines
- prevent data quality issues in data products

## When to choose
- you need automated, declarative data quality tests in Python data pipelines
- you want auto-generated data documentation and validation reports
- your team needs a shared, human-readable language for data expectations
- you integrate with supported sources like Snowflake, BigQuery, Spark, or Pandas

## When to avoid
- you need first-class Windows support, which is not officially supported
- you need a no-code GUI-only data quality tool
- you rely on Python versions outside 3.10-3.13
- you need guaranteed compatibility with niche data sources like Clickhouse or Teradata

## Facets
- artifact type: library
- maturity: active
- function: testing, data-science, etl, monitoring, analytics
- domain: data-science, analytics, developer-tools
- platform: python, cli
- tags: data-quality, data-validation, data-profiling, expectations, data-unit-tests, data-documentation, pipeline-testing, mlops, data-engineering, macos, linux

## Member repositories
- fivetran/great_expectations (main) score 95

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:49.620829+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:15:21.681334+00:00, confidence not recorded.
  - readme: https://github.com/fivetran/great_expectations (fetched 2026-08-28T04:10:49.620829+00:00, sha 60ae603968fe)
  - homepage: https://docs.greatexpectations.io/ (fetched 2026-08-29T08:13:39.092218+00:00, sha 9f53aa4a0470)
  - site_page: https://docs.greatexpectations.io/docs/0.18/core/introduction/introduction (fetched 2026-08-29T08:13:39.094626+00:00, sha d88a17bf0787)
  - site_page: https://docs.greatexpectations.io/docs/core/introduction (fetched 2026-08-29T08:13:39.096518+00:00, sha 6b75b366c263)
  - site_page: https://docs.greatexpectations.io/docs/reference/learn (fetched 2026-08-29T08:13:39.098047+00:00, sha a4f97819df3b)
  - site_page: https://docs.greatexpectations.io/docs/reference (fetched 2026-08-29T08:13:39.099507+00:00, sha 97e2755a74c6)
  - site_page: https://docs.greatexpectations.io/docs/help/get_support (fetched 2026-08-29T08:13:39.100916+00:00, sha d51d12348d3b)
  - site_page: https://docs.greatexpectations.io/docs/help/compatibility_reference (fetched 2026-08-29T08:13:39.102402+00:00, sha 8ec702459b69)
- Data as of 2026-08-30T08:39:29.467469+00:00.