Getting started with Great Expectations¶
Welcome to Great Expectations! This tutorial will help you set up your first local deployment of Great Expectations that contains a small Expectation Suite to validate some sample data. We’ll also introduce important concepts, with links to detailed material you can dig into later.
The tutorial will walk you throw the following steps - scroll to the bottom of this page and click the “Next” button to jump to the start of the tutorial!
In Great Expectations, your Data Context manages your project configuration. Using a DataContext is almost always the fastest way to get up and running, even though some teams don’t need every component of a DataContext.
In this first step, we will introduce you to the data and help you initialize a Data Context.
Once you have a Data Context, you’ll want to connect to data. In Great Expectations, Datasources simplify connections, by managing configuration and providing a consistent, cross-platform API for referencing data.
In this section, you will learn how to configure your first Datasource.
Expectations are the key concept in Great Expectations, a flexible, declarative language for describing the expected characteristics of data.
In this section, you will create your first Expectation Suite using the built-in automated profiler.
One of Great Expectations’ core promises is that your tests and documentation will always stay in sync, because docs and tests are both compiled from the same Expectations.
To see how this works, this section will show you how to set up a local static website for your data documentation. Later, you’ll be able to host the site remotely, or integrate content generated by Great Expectations into an metadata store.
In this section, we will show you how to use the Expectation Suite you’ve previously created to validate a new batch of data.
By this point, you’ll have your first, working local deployment of Great Expectations. As an optional next step, you can explore customizing your deployment.
Data Contexts make this modular, so that you can add or swap out one component at a time. Most of these changes are quick, incremental steps—so you can upgrade from a basic demo deployment to a full production deployment at your own pace, and be confident that your Data Context will continue to work at every step along the way.