Version: 0.18.3

Manage Checkpoints

A Checkpoint validates Expectation Suite data. After you create a Checkpoint to validate data, you can save and reuse the Checkpoint.

To learn more about Checkpoints, see Checkpoint.

Prerequisites

You have set up your environment and the GX Agent is running.
You have a Data Asset.
You have created an Expectation.

Add a Checkpoint

In Jupyter Notebook, run the following code to import the great_expectations module and the existing Data Context:
Jupyter Notebook
```
import great_expectations as gx
context = gx.get_context()
```

Run the following code to retrieve the Expectation Suite:

Jupyter Notebook
expectation_suite = context.get_expectation_suite(expectation_suite_name=<expectation_name>)

Run the following code to assign a name to the Checkpoint:
Jupyter Notebook
```
checkpoint_name = <checkpoint_name> 
```

Run the following code to define the Checkpoint configuration.

Jupyter Notebook
checkpoint_config = {
    "name": checkpoint_name,
    "validations": [{
        "expectation_suite_name": expectation_suite.expectation_suite_name,
         "expectation_suite_ge_cloud_id": expectation_suite.ge_cloud_id,
         "batch_request": {
            "datasource_name": "<data_source_name>",
            "data_asset_name": "<data_asset_name>",
         },
    }],
} 

Replace data_source_name and data_asset_name with the names of an existing Data Source and Data Asset. If you haven't connected to a Data Source and created a Data Asset, see Manage Data Assets.

Run the following code to add the Checkpoint:

Jupyter Notebook
checkpoint = context.add_or_update_checkpoint(**checkpoint_config) 

Optional. Run the following code to confirm the Checkpoint name:
Jupyter Notebook
```
print(checkpoint) 
```
Optional. Run the following code to run the Checkpoint:
Jupyter Notebook
```
result = checkpoint.run() 
```

Run a Checkpoint

In GX Cloud, click Checkpoints.
Optional. To run a Checkpoint on a failing Checkpoint, click Failures Only.
Optional. To run a specific Checkpoint, select it in the Checkpoints pane.
Click Run Checkpoint for the Checkpoint you want to run.

Edit a Checkpoint name

In GX Cloud, click Checkpoints.
Click Edit Checkpoint in the Checkpoints list for the Checkpoint you want to edit.
Enter a new name for the Checkpoint and then click Save.
Update the Checkpoint name in all code that included the previous Checkpoint name.

Edit a Checkpoint configuration

In Jupyter Notebook, run the following code to import the great_expectations module and the existing Data Context:
Jupyter Notebook
```
import great_expectations as gx
context = gx.get_context()
```

Run the following Python code to retrieve the Checkpoint:

Jupyter Notebook
retrieved_checkpoint = context.get_checkpoint(name="<checkpoint_name>") 

Edit the Checkpoint configuration.
Run the following code to update the Checkpoint configuration:
Jupyter Notebook
```
checkpoint = context.add_or_update_checkpoint(**checkpoint_config) 
```

Delete a Checkpoint

In GX Cloud, click Checkpoints.
Click Delete Checkpoint for the Checkpoint you want to delete.
Click Delete.

Prerequisites​

Add a Checkpoint​

Run a Checkpoint​

Edit a Checkpoint name​

Edit a Checkpoint configuration​

Delete a Checkpoint​