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                                                About GX Core

                                              GX Core combines the collective wisdom of thousands of community members with a proven track record in data quality deployments worldwide, wrapped into a super-simple package for data teams.

                                              Its powerful technical tools start with Expectations: expressive and extensible unit tests for your data. Expectations foster collaboration by giving teams a common language to express data quality tests in an intuitive way. You can automatically generate documentation for each set of validation results, making it easy for everyone to stay on the same page. This not only simplifies your data quality processes, but helps preserve your organization’s institutional knowledge about its data.

                                              Learn more about how data teams are using GX Core in our featured case studies.

                                                Integration support policy

                                              GX Core supports Python 3.10 through 3.13. Experimental support for Python 3.14 and later can be enabled by setting a GX_PYTHON_EXPERIMENTAL environment variable when installing great_expectations.

                                              For data sources and other integrations that GX supports, see the compatibility reference for additional information.

                                                Get started

                                              GX recommends deploying GX Core within a virtual environment. For more information about getting started with GX Core, see Introduction to GX Core.

                                               1. Run the following command in an empty base directory inside a Python virtual environment to install GX Core:

                                                    pip install great_expectations

                                               2. Run the following command to import the great_expectations module and create a Data Context:

                                                    import great_expectations as gx
                                                    
                                                    context = gx.get_context()

                                                Get support from GX and the community

                                              They are listed in the order in which GX is prioritizing the support issues:

                                               1. Issues and PRs in the GX GitHub repository
                                               2. Questions posted to the GX Core Discourse forum
                                               3. Questions posted to the GX Slack community channel

                                                Contribute

                                              We truly value the contributions of our community and always welcome pull requests. PRs are encouraged for both bug fixes and new features. For feature requests, we ask that you first open an issue for discussion to ensure the feature fits within the vision for GX Core and to align on the approach so that your time and effort are well spent. Thank you for being a crucial part of GX Core!

                                              See CONTRIBUTING.md for details on how to propose a change, claim an issue, and submit a pull request.

                                                Code of conduct

                                              Everyone interacting in GX Core project codebases, Discourse forums, Slack channels, and email communications is expected to adhere to the GX Community Code of Conduct.

                                        About

                                        Always know what to expect from your data.

                                          docs.greatexpectations.io/

                                        Topics

                                          cleandatadata-engineeringdata-profilersdata-profilingdata-qualitydata-sciencedata-unit-testsdatacleanerdatacleaningdataqualitydataunittestedaexploratory-analysisexploratory-data-analysisexploratorydataanalysismlopspipelinepipeline-debtpipeline-testingpipeline-tests

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