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- ![ data_assistant_plot_expectations_and_metrics] ( ./readme_assets/data_assistant_plot_expectations_and_metrics.png )
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+ ![ data_assistant_plot_expectations_and_metrics] ( ./docs/ readme_assets/data_assistant_plot_expectations_and_metrics.png )
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### Unified understanding
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Checkpoints are a transparent, central, and automatable mechanism for testing Expectations and evaluating your data quality. Every Checkpoint run produces human-readable Data Docs reporting the results. You can also configure Checkpoints to take Actions based on the results of the evaluation, like sending alerts and preventing low-quality data from moving further in your pipelines.
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- ![ Image of data contact support] ( ./readme_assets/data-contract-support.png )
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### Readable for collaboration
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Everyone stays on the same page about your data quality with GX’s inspectable, shareable, and human-readable Data Docs. You can publish Data Docs to the locations where you need them in a variety of formats, making it easy to integrate Data Docs into your existing data catalogs, dashboards, and other reporting and data governance tools.
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- ![ Image of data docs] ( ./readme_assets/datadocs-update.jpg )
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Quick start
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<a href="https://docs.greatexpectations.io/docs/integrations/integration_datahub/">
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<a href="https://docs.greatexpectations.io/docs/deployment_patterns/how_to_use_great_expectations_in_aws_glue/">
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<a href="https://docs.greatexpectations.io/docs/guides/connecting_to_your_data/database/athena/">
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<a href="https://docs.greatexpectations.io/docs/guides/connecting_to_your_data/database/redshift/">
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<a href="https://docs.greatexpectations.io/docs/guides/setup/configuring_data_docs/how_to_host_and_share_data_docs_on_amazon_s3/">
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<a href="https://docs.greatexpectations.io/docs/deployment_patterns/how_to_use_great_expectations_with_google_cloud_platform_and_bigquery/">
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<a href="https://docs.greatexpectations.io/docs/deployment_patterns/how_to_use_great_expectations_in_deepnote/">
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<a href="https://docs.greatexpectations.io/docs/deployment_patterns/how_to_use_great_expectations_with_google_cloud_platform_and_bigquery/">
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<a href="https://docs.greatexpectations.io/docs/guides/setup/configuring_data_docs/how_to_host_and_share_data_docs_on_azure_blob_storage/">
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<a href="https://docs.greatexpectations.io/docs/guides/connecting_to_your_data/database/mssql">
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Great Expectations is _ not_ a data versioning tool. If you want to bring your data itself under version control, check out tools like [ DVC] ( https://dvc.org/ ) and [ Quilt] ( https://github.com/quiltdata/quilt ) .
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- Great Expectations is _ not_ a language-agnostic platform. Instead, it follows the philosophy of “take the compute to the data” by using the popular Python language to support native execution of Expectations in pandas, SQL (via SQLAlchemy), and Spark environments.
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Great Expectations is _ not_ exclusive to Python programming environments. It can be invoked from the command line without a Python environment. However, if you’re working into another ecosystem, you may want to explore ecosystem-specific alternatives such as [ assertR] ( https://github.com/ropensci/assertr ) (for R environments) or [ TFDV] ( https://www.tensorflow.org/tfx/guide/tfdv ) (for Tensorflow environments).
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