fivetran/great_expectations
Always know what to expect from your data. observed · 2026-08-28
Health v2 · maintenance only
95/100
- Activity 99
- Release rhythm 86
- Longevity 100
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.
- gap_med: 7
- age_days: 3279
- days_rel: 14
- days_push: 7
- n_releases_24m: 90
Adoption not part of the score
11740 stars · 1813 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded
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
library · maturity active
testing data-science etl monitoring analytics data-science analytics developer-tools python cli data-quality data-validation data-profiling expectations data-unit-tests data-documentation pipeline-testing mlops data-engineering macos linux
8 sources
- readme: https://github.com/fivetran/great_expectations · fetched 2026-08-28 · 60ae603968fe
- homepage: https://docs.greatexpectations.io/ · fetched 2026-08-29 · 9f53aa4a0470
- site_page: https://docs.greatexpectations.io/docs/0.18/core/introduction/introduction · fetched 2026-08-29 · d88a17bf0787
- site_page: https://docs.greatexpectations.io/docs/core/introduction · fetched 2026-08-29 · 6b75b366c263
- site_page: https://docs.greatexpectations.io/docs/reference/learn · fetched 2026-08-29 · a4f97819df3b
- site_page: https://docs.greatexpectations.io/docs/reference · fetched 2026-08-29 · 97e2755a74c6
- site_page: https://docs.greatexpectations.io/docs/help/get_support · fetched 2026-08-29 · d51d12348d3b
- site_page: https://docs.greatexpectations.io/docs/help/compatibility_reference · fetched 2026-08-29 · 8ec702459b69
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| fivetran/great_expectations | main | 95 |
For agents
markdown · JSON · MCP: product_card(name="fivetran/great_expectations")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem