pandas.DataFrameas part of its pipelines.
configure_zenml_storesattribute in the Data Validator. The downside is that you will only be able to run pipelines locally with this setup, given that the Great Expectations configuration is a file on your local machine.
great_expectations.yamlconfiguration file is located:
configure_zenml_stores: if set, ZenML will automatically update the Great Expectation configuration to include Metadata Stores that use the Artifact Store as a backend. If neither
context_configare set, this is the default behavior. You can set this flag to use the ZenML Artifact Store as a backend for Great Expectations with any of the deployment methods described above. Note that ZenML will not copy the information in your existing Great Expectations stores (e.g. Expectation Suites, Validation Results) in the ZenML Artifact Store. This is something that you will have to do yourself.
configure_local_docs: set this flag to configure a local Data Docs site where Great Expectations docs are generated and can be visualized locally. Use this in case you don't already have a local Data Docs site in your existing Great Expectations configuration.
UserConfigurableProfileron an input
pandas.DataFramedataset. The generated Expectation Suite is saved in the Great Expectations Expectation Store, but also returned as an
ExpectationSuiteartifact that is versioned and saved in the ZenML Artifact Store. The step automatically rebuilds the Data Docs.
pandas.DataFramedataset by running an existing Expectation Suite on it. The validation results are saved in the Great Expectations Validation Store, but also returned as an
CheckpointResultartifact that is versioned and saved in the ZenML Artifact Store. The step automatically rebuilds the Data Docs.
pandas.DataFramedataset and a boolean
conditionand it returns a Great Expectations
CheckpointResultobject. The boolean
conditionis only used as a means of ordering steps in a pipeline (e.g. if you must force it to run only after the data profiling step generates an Expectation Suite):
CheckpointResultobjects in its Artifact Store. To use the Great Expectations configuration managed by ZenML while interacting with the Great Expectations library directly, you need to use the Data Context managed by ZenML instead of the default one provided by Great Expectations, e.g.: