ddd
This document explains how we tackle the different parts of the Great Expectations Lifecycle. 

## The Lifecycle

The lifecycle we refer to encompasses all the steps from deciding that you need to recurrently validate data somewhere to decomissioning the validation. It encompasses three main stages:
- The setup: deciding what is expected of the data, designing the checkpoint, testing it out and putting it into production.
- The operation and iteration: operating your data validation and updating it across time as business and data environments change.
- The teardown: decomissioning a data validation that is no longer needed. 


## Setup

Setting up data validation on an ETL or other automated job means creating a new Great Expectations checkpoint. Having datasources and expectations suites configured and ready to use are prerequisites to creating the checkpoint.

### Creating a Datasource


### Creating an Expectation Suite


## Creating a Checkpoint

## Operation and Iteration

### Integrating the checkpoint in a Prefect Flow
- Staging and quarantine strategy
- Using transactions to rollback
- Slack alerts
- Checking validation docs after failure


### 



## Glossary

| Term       | Meaning                                                                                              |
| ---------- | ---------------------------------------------------------------------------------------------------- |
| Checkpoint | A recipe that ties together datasources, expectation suites and actions to execute after validating. |
|            |                                                                                                      |