{"categories":[{"label":"Software Development","url":"https://skillfed.io/packages/category/software-development/16"},{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/7"},{"label":"Testing","url":"https://skillfed.io/packages/category/software-development-testing/5"}],"enrichment":{"capability":"GX Core provides data quality testing through Expectations\u2014expressive unit tests for data\u2014and automatically generates validation documentation to help teams collaborate on data quality standards.","skillfed_tags":["data-quality","data-validation","testing-framework"],"use_cases":["Define and enforce data quality rules in ETL pipelines before data reaches downstream systems.","Generate automated documentation of data validation results for compliance and audit trails.","Establish shared data quality standards across teams using a common Expectations language.","Monitor data source quality over time by running the same Expectations repeatedly.","Catch data anomalies early by validating schema, nullability, and value ranges on ingestion."],"what_it_does":"GX Core is a data quality testing framework built around Expectations\u2014a declarative way to express data validation rules as unit tests. It lets data teams define what good data looks like, run those tests against pipelines and datasets, and automatically generate documentation of validation results. The framework emphasizes collaboration by giving teams a shared language for data quality standards and helps preserve institutional knowledge about data shape and content.\n\nThe package ships with 29 runtime dependencies spanning the Jupyter ecosystem (notebook, ipywidgets, IPython), data processing (pandas, numpy, scipy), serialization (jsonschema, pydantic, ruamel.yaml), and visualization (altair). It's designed to run in virtual environments and integrates with various data sources through a compatibility matrix. The core workflow is simple: import the module, create a Data Context, define Expectations, and validate data against them.","worth_installing":"Yes. Actively maintained with no known vulnerabilities, uses a permissive Apache-2.0 license, and installs cleanly. It solves a real problem\u2014collaborative data quality testing\u2014with a mature approach. The 29 dependencies are standard data-science libraries. Install it if your workflow involves validating data quality at any stage of a pipeline."},"id":"acryl-great-expectations","links":{"html":"https://skillfed.io/packages/acryl-great-expectations","md":"https://skillfed.io/packages/acryl-great-expectations.md","pypi":"https://pypi.org/project/acryl-great-expectations/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2025-05-02","license_spdx":null,"license_treatment":"permissive","name":"acryl-great-expectations","python_support":"unspecified","summary":"Always know what to expect from your data."},"popularity":{"monthly_downloads":125372,"position":11823,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.15.50.1"}