Start here to learn how to connect to data, create Expectations, validate data, and review Validation Results. This is an ideal place to start if you're new to GX Core and want to experiment with features …

Great Expectations documentation Learn everything you need to know about GX Cloud and GX Core

Great Expectations (GX) is a framework for describing data using expressive tests and then validating that the data meets test criteria. GX Core is a Python library that provides a programmatic interface …

Expectations make implicit assumptions about your data explicit, and they provide a flexible, declarative language for describing expected behavior. They can help you better understand your data and help …

This workflow is solely intended for interactively creating Expectations and engaging in data Exploration. For further information on using an individual Batch to test Expectations see Test an Expectation.

Learn about key Great Expectations (GX) Core components and workflows. Use the GX Core Python library and provided sample data to create a data validation workflow.

Create Expectations with a Python interpreter or a script and then use interactive feedback to validate them with batch data.

Great Expectations is Python-based. You can invoke it from the command line without using a Python programming environment, but if you’re working in another ecosystem, other tools might be a better …

It also contains your Validation Results and the metrics associated with them, and it provides access to those objects in Python, along with other helper functions. All scripts that utilize GX Core should start …

To use Great Expectations (GX) you need to install Python and the GX Core Python library. GX also recommends you set up a virtual environment for your GX Python projects.