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zenml integration install great_expectations -yDepending on how you configure the Great Expectations Data Validator, it can reduce or even completely eliminate the complexity associated with setting up the store backends for Great Expectations. If you're only looking for a quick and easy way of adding Great Expectations to your stack and are not concerned with the configuration details, you can simply run:

# Register the Great Expectations data validator

zenml data-validator register ge_data_validator --flavor=great_expectations

# Register and set a stack with the new data validator

zenml stack register custom_stack -dv ge_data_validator ... --set

If you already have a Great Expectations deployment, you can configure the Great Expectations Data Validator to reuse or even replace your current configuration. You should consider the pros and cons of every deployment use-case and choose the one that best fits your needs:

let ZenML initialize and manage the Great Expectations configuration. The Artifact Store will serve as a storage backend for all the information that Great Expectations needs to persist (e.g. Expectation Suites, Validation Results). However, you will not be able to setup new Data Sources, Metadata Stores or Data Docs sites. Any changes you try and make to the configuration through code will not be persisted and will be lost when your pipeline completes or your local process exits.

use ZenML with your existing Great Expectations configuration. You can tell ZenML to replace your existing Metadata Stores with the active ZenML Artifact Store by setting the configure_zenml_stores attribute 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.