How to use Great Expectations with Amazon Web Services using Redshift

Great Expectations can work within many frameworks. In this guide you will be shown a workflow for using Great Expectations with AWS and cloud storage. You will configure a local Great Expectations project to store Expectations, Validation Results, and Data Docs in Amazon S3 buckets. You will further configure Great Expectations to access data from a Redshift database.

This guide will demonstrate each of the steps necessary to go from installing a new instance of Great Expectations to Validating your data for the first time and viewing your Validation Results as Data Docs.

Prerequisites

• Python 3. To download and install Python, see Python downloads.
• The AWS CLI. To download and install the AWS CLI, see Installing or updating the latest version of the AWS CLI.
• AWS credentials. See Configuring the AWS CLI.
• Permissions to install the Python packages (boto3 and great_expectations) with pip.
• An S3 bucket and prefix to store Expectations and Validation Results.

Steps

Part 1: Setup

1.1 Ensure that the AWS CLI is ready for use

1.1.1 Verify that the AWS CLI is installed

You can verify that the AWS CLI has been installed by running the command:

Terminal command

aws --version

If this command does not respond by informing you of the version information of the AWS CLI, you may need to install the AWS CLI or otherwise troubleshoot your current installation. For detailed guidance on how to do this, please refer to Amazon's documentation on how to install the AWS CLI)

1.1.2 Verify that your AWS credentials are properly configured

Run the following command in the AWS CLI to verify that your AWS credentials are properly configured:

Terminal command

aws sts get-caller-identity

When your credentials are properly configured, your UserId, Account and Arn are returned. If your credentials are not configured correctly, an error message appears.

If an error message appears, or if you couldn't use the AWS CLI to verify your credentials configuration, see Configuring the AWS CLI.

1.2 Prepare a local installation of Great Expectations

1.2.1 Verify that your Python version meets requirements

First, check the version of Python that you have installed. As of this writing, Great Expectations supports versions 3.7 through 3.10 of Python.

You can check your version of Python by running:

Terminal command

python --version

If this command returns something other than a Python 3 version number (like Python 3.X.X), you may need to try this:

Terminal command

python3 --version

If you do not have Python 3 installed, please refer to python.org for the necessary downloads and guidance to perform the installation.

1.2.2 Create a virtual environment for your Great Expectations project

Once you have confirmed that Python 3 is installed locally, you can create a virtual environment with venv before installing your packages with pip.

Python Virtual Environments

We have chosen to use venv for virtual environments in this guide, because it is included with Python 3. You are not limited to using venv, and can just as easily install Great Expectations into virtual environments with tools such as virtualenv, pyenv, etc.

Depending on whether you found that you needed to run python or python3 in the previous step, you will create your virtual environment by running either:

Terminal command

python -m venv my_venv

or

Terminal command

python3 -m venv my_venv

This command will create a new directory called my_venv where your virtual environment is located. In order to activate the virtual environment run:

Terminal command

source my_venv/bin/activate

tip

You can name your virtual environment anything you like. Simply replace my_venv in the examples above with the name that you would like to use.

1.2.3 Ensure you have the latest version of pip

Once your virtual environment is activated, you should ensure that you have the latest version of pip installed.

Pip is a tool that is used to easily install Python packages. If you have Python 3 installed you can ensure that you have the latest version of pip by running either:

Terminal command

python -m ensurepip --upgrade

or

Terminal command

python3 -m ensurepip --upgrade

1.2.4 Install boto3

Python interacts with AWS through the boto3 library. Great Expectations makes use of this library in the background when working with AWS. Although you won't use boto3 directly, you'll need to install it in your virtual environment.

Run one of the following pip commands to install boto3 in your virtual environment:

Terminal command

python -m pip install boto3

or

Terminal command

python3 -m pip install boto3

To set up boto3 with AWS, and use boto3 from within Python, see the Boto3 documentation.

1.2.5 Install Great Expectations

You can use pip to install Great Expectations by running the appropriate pip command below:

Terminal command

python -m pip install great_expectations

or

Terminal command

python3 -m pip install great_expectations

1.2.6 Verify that Great Expectations installed successfully

You can confirm that installation worked by running:

Terminal command

great_expectations --version

This should return something like:

Terminal output

great_expectations, version 0.16.15

1.2.7 Install additional dependencies for Redshift

To use connect to your Redshift database, Great Expectations will require the installation of additional dependencies. Fortunately, it is simple to install the necessary dependencies for Redshift by using pip and running the following from your terminal:

pip install sqlalchemy sqlalchemy-redshift psycopg2

# or if on macOS:
pip install sqlalchemy sqlalchemy-redshift psycopg2-binary

caution

As of this writing, Great Expectations is not compatible with SQLAlchemy version 2 or greater. We recommend using the latest non-version-2 release.

1.3 Create your Data Context

The simplest way to create a new Data ContextThe primary entry point for a Great Expectations deployment, with configurations and methods for all supporting components. is by using the create() method.

From a Notebook or script where you want to deploy Great Expectations run the following command. Here the full_path_to_project_directory can be an empty directory where you intend to build your Great Expectations configuration.:

import great_expectations as gx

context = gx.data_context.FileDataContext.create(full_path_to_project_directory)

1.4 Configure your Expectations Store on Amazon S3

1.4.1 Identify your Data Context Expectations Store

Your Expectation StoreA connector to store and retrieve information about collections of verifiable assertions about data. configuration is in your Data ContextThe primary entry point for a Great Expectations deployment, with configurations and methods for all supporting components..

The following section in your Data ContextThe primary entry point for a Great Expectations deployment, with configurations and methods for all supporting components. great_expectations.yml file tells Great Expectations to look for Expectations in a Store named expectations_store:

stores:
  expectations_store:
    class_name: ExpectationsStore
    store_backend:
      class_name: TupleFilesystemStoreBackend
      base_directory: expectations/

expectations_store_name: expectations_store

The default base_directory for expectations_store is expectations/.

1.4.2 Update your configuration file to include a new Store for Expectations on Amazon S3

To manually add an Expectations StoreA connector to store and retrieve information about collections of verifiable assertions about data. to your configuration, add the following configuration to the stores section of your great_expectations.yml file:

stores:
  expectations_S3_store:
    class_name: ExpectationsStore
    store_backend:
      class_name: TupleS3StoreBackend
      bucket: <your>
      prefix: <your>

expectations_store_name: expectations_S3_store

As shown in the previous example, you need to change the default store_backend settings to make the Store work with S3. The class_name is set to TupleS3StoreBackend, bucket is the address of your S3 bucket, and prefix is the folder in your S3 bucket where Expectations are located.

The following example shows the additional options that are available to customize TupleS3StoreBackend:

File contents: great_expectations.yml

class_name: ExpectationsStore
store_backend:
  class_name: TupleS3StoreBackend
  bucket: '<your_s3_bucket_name>'
  prefix: '<your_s3_bucket_folder_name>'
  boto3_options:
    endpoint_url: ${S3_ENDPOINT} # Uses the S3_ENDPOINT environment variable to determine which endpoint to use.
    region_name: '<your_aws_region_name>'

In the previous example, the Store name is expectations_S3_store. If you use a personalized Store name, you must also update the value of the expectations_store_name key to match the Store name. For example:

File contents: great_expectations.yml

expectations_store_name: expectations_S3_store

When you update the expectations_store_name key value, Great Expectations uses the new Store for Validation Results.

Add the following code to great_expectations.yml to configure the IAM user:

File contents: great_expectations.yml

class_name: ExpectationsStore
store_backend:
  class_name: TupleS3StoreBackend
  bucket: '<your_s3_bucket_name>'
  prefix: '<your_s3_bucket_folder_name>'
  boto3_options:
    aws_access_key_id: ${AWS_ACCESS_KEY_ID} # Uses the AWS_ACCESS_KEY_ID environment variable to get aws_access_key_id.
    aws_secret_access_key: ${AWS_ACCESS_KEY_ID}
    aws_session_token: ${AWS_ACCESS_KEY_ID}

Add the following code to great_expectations.yml to configure the IAM Assume Role:

File contents: great_expectations.yml

class_name: ExpectationsStore
store_backend:
  class_name: TupleS3StoreBackend
  bucket: '<your_s3_bucket_name>'
  prefix: '<your_s3_bucket_folder_name>'
  boto3_options:
    assume_role_arn: '<your_role_to_assume>'
    region_name: '<your_aws_region_name>'
    assume_role_duration: session_duration_in_seconds

caution

If you are also storing Validations in S3 or DataDocs in S3, make sure that the prefix values are disjoint and one is not a substring of the other.

1.4.3 (Optional) Copy existing Expectation JSON files to the Amazon S3 bucket

If you are converting an existing local Great Expectations deployment to one that works in AWS, you might have Expectations saved that you want to transfer to your S3 bucket.

To copy Expectations into Amazon S3, use the aws s3 sync command as shown in the following example:

Terminal command

aws s3 sync '<base_directory>' s3://'<your_s3_bucket_name>'/'<your_s3_bucket_folder_name>'

The base_directory is set to expectations/ by default.

In the following example, the Expectations exp1 and exp2 are copied to Amazon S3 and a confirmation message is returned:

Terminal output

upload: ./exp1.json to s3://'<your_s3_bucket_name>'/'<your_s3_bucket_folder_name>'/exp1.json
upload: ./exp2.json to s3://'<your_s3_bucket_name>'/'<your_s3_bucket_folder_name>'/exp2.json

1.4.4 (Optional) Verify that copied Expectations can be accessed from Amazon S3

If you copied your existing Expectation Suites to the S3 bucket, run the following Python code to confirm that Great Expectations can find them:

import great_expectations as gx

context = gx.get_context()
context.list_expectation_suite_names()

The Expectations you copied to S3 are returned as a list. Expectations that weren't copied to the new Store aren't listed.

1.5 Configure your Validation Results Store on Amazon S3

1.5.1 Identify your Data Context's Validation Results Store

Your Validation Results StoreA connector to store and retrieve information about objects generated when data is Validated against an Expectation Suite. configuration is in your Data ContextThe primary entry point for a Great Expectations deployment, with configurations and methods for all supporting components..

The following section in your Data ContextThe primary entry point for a Great Expectations deployment, with configurations and methods for all supporting components. great_expectations.yml file tells Great Expectations to look for Validation Results in a Store named validations_store. It also creates a ValidationsStore named validations_store that is backed by a Filesystem and stores Validation Results under the base_directory uncommitted/validations (the default).

stores:
  validations_store:
    class_name: ValidationsStore
    store_backend:
      class_name: TupleFilesystemStoreBackend
      base_directory: uncommitted/validations/

validations_store_name: validations_store

1.5.2 Update your configuration file to include a new Store for Validation Results on Amazon S3

To manually add a Validation Results Store, add the following configuration to the stores section of your great_expectations.yml file:

stores:
  validations_S3_store:
    class_name: ValidationsStore
    store_backend:
      class_name: TupleS3StoreBackend
      bucket: <your>
      prefix: <your>

As shown in the previous example, you need to change the default store_backend settings to make the Store work with S3. The class_name is set to TupleS3StoreBackend, bucket is the address of your S3 bucket, and prefix is the folder in your S3 bucket where Validation Results are located.

The following example shows the additional options that are available to customize TupleS3StoreBackend:

File contents: great_expectations.yml

class_name: ValidationsStore
store_backend:
  class_name: TupleS3StoreBackend
  bucket: '<your_s3_bucket_name>'
  prefix: '<your_s3_bucket_folder_name>'
  boto3_options:
    endpoint_url: ${S3_ENDPOINT} # Uses the S3_ENDPOINT environment variable to determine which endpoint to use.
    region_name: '<your_aws_region_name>'

In the previous example, the Store name is validations_S3_store. If you use a personalized Store name, you must also update the value of the validations_store_name key to match the Store name. For example:

validations_store_name: validations_S3_store

When you update the validations_store_name key value, Great Expectations uses the new Store for Validation Results.

Add the following code to great_expectations.yml to configure the IAM user:

File contents: great_expectations.yml

class_name: ValidationsStore
store_backend:
  class_name: TupleS3StoreBackend
  bucket: '<your_s3_bucket_name>'
  prefix: '<your_s3_bucket_folder_name>'
  boto3_options:
    aws_access_key_id: ${AWS_ACCESS_KEY_ID} # Uses the AWS_ACCESS_KEY_ID environment variable to get aws_access_key_id.
    aws_secret_access_key: ${AWS_ACCESS_KEY_ID}
    aws_session_token: ${AWS_ACCESS_KEY_ID}

Add the following code to great_expectations.yml to configure the IAM Assume Role:

File contents: great_expectations.yml

class_name: ValidationsStore
store_backend:
  class_name: TupleS3StoreBackend
  bucket: '<your_s3_bucket_name>'
  prefix: '<your_s3_bucket_folder_name>'
  boto3_options:
    assume_role_arn: '<your_role_to_assume>'
    region_name: '<your_aws_region_name>'
    assume_role_duration: session_duration_in_seconds

caution

If you are also storing ExpectationsA verifiable assertion about data. in S3 (How to configure an Expectation store to use Amazon S3), or DataDocs in S3 (How to host and share Data Docs on Amazon S3), then make sure the prefix values are disjoint and one is not a substring of the other.

1.5.3 (Optional) Copy existing Validation results to the Amazon S3 bucket

If you are converting an existing local Great Expectations deployment to one that works in AWS, you might have Validation Results saved that you want to transfer to your S3 bucket.

To copy Validation Results into Amazon S3, use the aws s3 sync command as shown in the following example:

Terminal input

aws s3 sync '<base_directory>' s3://'<your_s3_bucket_name>'/'<your_s3_bucket_folder_name>'

The base_directory is set to uncommitted/validations/ by default.

In the following example, the Validation Results Validation1 and Validation2 are copied to Amazon S3 and a confirmation message is returned:

Terminal output

upload: uncommitted/validations/val1/val1.json to s3://'<your_s3_bucket_name>'/'<your_s3_bucket_folder_name>'/val1.json
upload: uncommitted/validations/val2/val2.json to s3://'<your_s3_bucket_name>'/'<your_s3_bucket_folder_name>'/val2.json

1.6 Configure Data Docs for hosting and sharing from Amazon S3

1.6.1 Create an Amazon S3 bucket for your Data Docs

You can create an S3 bucket configured for a specific location using the AWS CLI. Make sure you modify the bucket name and region for your situation.

Terminal input

> aws s3api create-bucket --bucket data-docs.my_org --region us-east-1
{
    "Location": "/data-docs.my_org"
}

1.6.2 Configure your bucket policy to enable appropriate access

The example policy below enforces IP-based access - modify the bucket name and IP addresses for your situation. After you have customized the example policy to suit your situation, save it to a file called ip-policy.json in your local directory.

caution

Your policy should provide access only to appropriate users. Data Docs sites can include critical information about raw data and should generally not be publicly accessible.

File content: ip-policy.json

  {
    "Version": "2012-10-17",
    "Statement": [{
      "Sid": "Allow only based on source IP",
      "Effect": "Allow",
      "Principal": "*",
      "Action": "s3:GetObject",
      "Resource": [
        "arn:aws:s3:::data-docs.my_org",
        "arn:aws:s3:::data-docs.my_org/*"
      ],
      "Condition": {
        "IpAddress": {
          "aws:SourceIp": [
            "192.168.0.1/32",
            "2001:db8:1234:1234::/64"
          ]
        }
      }
    }
    ]
  }

tip

Because Data Docs include multiple generated pages, it is important to include the arn:aws:s3:::{your_data_docs_site}/* path in the Resource list along with the arn:aws:s3:::{your_data_docs_site} path that permits access to your Data Docs' front page.

REMINDER

Amazon Web Service's S3 buckets are a third party utility. For more (and the most up to date) information on configuring AWS S3 bucket policies, please refer to Amazon's guide on using bucket policies.

1.6.3 Apply the access policy to your Data Docs' Amazon S3 bucket

Run the following AWS CLI command to apply the policy:

Terminal input

> aws s3api put-bucket-policy --bucket data-docs.my_org --policy file://ip-policy.json

1.6.4 Add a new Amazon S3 site to the data_docs_sites section of your great_expectations.yml

The below example shows the default local_site configuration that you will find in your great_expectations.yml file, followed by the s3_site configuration that you will need to add. You may optionally remove the default local_site configuration completely and replace it with the new s3_site configuration if you would only like to maintain a single S3 Data Docs site.

data_docs_sites:
  local_site:
    class_name: SiteBuilder
    show_how_to_buttons: true
    store_backend:
      class_name: TupleFilesystemStoreBackend
      base_directory: uncommitted/data_docs/local_site/
    site_index_builder:
      class_name: DefaultSiteIndexBuilder
  S3_site:  # this is a user-selected name - you may select your own
    class_name: SiteBuilder
    store_backend:
      class_name: TupleS3StoreBackend
      bucket: <your>
    site_index_builder:
      class_name: DefaultSiteIndexBuilder

1.6.5 Test that your Data Docs configuration is correct by building the site

Use the following command: context.build_data_docs() to build and open your newly configured S3 Data Docs site.

context.build_data_docs()

Additional notes on hosting Data Docs from an Amazon S3 bucket

• Optionally, you may wish to update static hosting settings for your bucket to enable AWS to automatically serve your index.html file or a custom error file:

  > aws s3 website s3://data-docs.my_org/ --index-document index.html

• If you wish to host a Data Docs site in a subfolder of an S3 bucket, add the prefix property to the configuration snippet in step 4, immediately after the bucket property.

• If you wish to host a Data Docs site through a private DNS, you can configure a base_public_path for the Data Docs StoreA connector to store and retrieve information pertaining to Human readable documentation generated from Great Expectations metadata detailing Expectations, Validation Results, etc.. The following example will configure a S3 site with the base_public_path set to www.mydns.com. Data Docs will still be written to the configured location on S3 (for example https://s3.amazonaws.com/data-docs.my_org/docs/index.html), but you will be able to access the pages from your DNS (http://www.mydns.com/index.html in our example)

  data_docs_sites:
    s3_site:  # this is a user-selected name - you may select your own
      class_name: SiteBuilder
      store_backend:
        class_name: TupleS3StoreBackend
        bucket: data-docs.my_org  # UPDATE the bucket name here to match the bucket you configured above.
        base_public_path: http://www.mydns.com
      site_index_builder:
        class_name: DefaultSiteIndexBuilder
        show_cta_footer: true

Part 2: Connect to data

2.1 Instantiate your project's DataContext

The simplest way to create a new Data ContextThe primary entry point for a Great Expectations deployment, with configurations and methods for all supporting components. is by using the create() method.

From a Notebook or script where you want to deploy Great Expectations run the following command. Here the full_path_to_project_directory can be an empty directory where you intend to build your Great Expectations configuration.:

import great_expectations as gx

context = gx.data_context.FileDataContext.create(full_path_to_project_directory)

If you have already instantiated your DataContext in a previous step, this step can be skipped.

2.1.1 Determine your connection string

For this guide we will use a connection_string like this:

redshift+psycopg2://<USER_NAME>:<PASSWORD>@<HOST>:<PORT>/<DATABASE>?sslmode=<SSLMODE>

Note: Depending on your Redshift cluster configuration, you may or may not need the sslmode parameter. For more details, please refer to Amazon's documentation for configuring security options on Amazon Redshift.

Is there a more secure way to store my credentials than plain text in a connection string?

We recommend that database credentials be stored in the config_variables.yml file, which is located in the uncommitted/ folder by default, and is not part of source control.

For additional options on configuring the config_variables.yml file or additional environment variables, please see our guide on how to configure credentials.

2.2 Add Datasource to your DataContext

Creating a Redshift Datasource is as simple as providing the add_or_update_sql(...) method a name by which to reference it in the future and the connection_string with which to access it.

datasource_name = "version-0.16.16 my_redshift_datasource"
connection_string = "redshift+psycopg2://<user_name>:<password>@<host>:<port>/<database>?sslmode=<sslmode>"

With these two values, we can create our Datasource:

datasource = context.sources.add_or_update_sql(
    name=datasource_name,
    connection_string=connection_string,
)

2.3. Connect to a specific set of data with a Data Asset

Now that our Datasource has been created, we will use it to connect to a specific set of data in the database it is configured for. This is done by defining a Data Asset in the Datasource. A Datasource may contain multiple Data Assets, each of which will serve as the interface between GX and the specific set of data it has been configured for.

With SQL databases, there are two types of Data Assets that can be used. The first is a Table Data Asset, which connects GX to the data contained in a single table in the source database. The other is a Query Data Asset, which connects GX to the data returned by a SQL query. We will demonstrate how to create both of these in the following steps.

How many Data Assets can my Datasource contain?

Although there is no set maximum number of Data Assets you can define for a datasource, there is a functional minimum. In order for GX to retrieve data from your Datasource you will need to create at least one Data Asset.

We will indicate a table to connect to with a Table Data Asset. This is done by providing the add_table_asset(...) method a name by which we will reference the Data Asset in the future and a table_name to specify the table we wish the Data Asset to connect to.

table_asset = datasource.add_table_asset(name="my_table_asset", table_name="version-0.16.16 taxi_data")

To indicate the query that provides data to connect to we will define a Query Data Asset. This done by providing the add_query_asset(...) method a name by which we will reference the Data Asset in the future and a query which will provide the data we wish the Data Asset to connect to.

query_asset = datasource.add_query_asset(
    name="version-0.16.16 my_query_asset", query="SELECT * from taxi_data"
)

2.4 Test your new Datasource

Verify your new DatasourceProvides a standard API for accessing and interacting with data from a wide variety of source systems. by loading data from it into a ValidatorUsed to run an Expectation Suite against data. using a Batch RequestProvided to a Datasource in order to create a Batch..

request = table_asset.build_batch_request()

context.add_or_update_expectation_suite(expectation_suite_name="version-0.16.16 test_suite")

validator = context.get_validator(
    batch_request=request, expectation_suite_name="version-0.16.16 test_suite"
)

print(validator.head())

Part 3: Create Expectations

3.1: Prepare a Batch Request, empty Expectation Suite, and Validator

When we tested our Datasource in step 2.3: Test your new Datasource we also created all of the components we need to begin creating Expectations: A Batch Request to provide sample data we can test our new Expectations against, an empty Expectation Suite to contain our new Expectations, and a Validator to create those Expectations with.

We can reuse those components now. Alternatively, you may follow the same process that we did before and define a new Batch Request, Expectation Suite, and Validator if you wish to use a different Batch of data as the reference sample when you are creating Expectations or if you wish to use a different name than test_suite for your Expectation Suite.

3.2: Use a Validator to add Expectations to the Expectation Suite

There are many Expectations available for you to use. To demonstrate creating an Expectation through the use of the Validator we defined earlier, here are examples of the process for two of them:

validator.expect_column_values_to_not_be_null(column="passenger_count")
validator.expect_column_values_to_be_between(
    column="congestion_surcharge", min_value=0, max_value=1000
)

Each time you evaluate an Expectation (e.g. via validator.expect_*) two things will happen. First, the Expectation will immediately be Validated against your provided Batch of data. This instant feedback helps to zero in on unexpected data very quickly, taking a lot of the guesswork out of data exploration. Second, the Expectation configuration will be stored in the Expectation Suite you provided when the Validator was initialized.

You can also create Expectation Suites using a Data Assistant to automatically create expectations based on your data or manually using domain knowledge and without inspecting data directly.

To find out more about the available Expectations, please see our Expectations Gallery.

3.3: Save the Expectation Suite

When you have run all of the Expectations you want for this dataset, you can call validator.save_expectation_suite() to save the Expectation Suite (all of the unique Expectation Configurations from each run of validator.expect_*)for later use in a Checkpoint.

validator.save_expectation_suite(discard_failed_expectations=False)

Part 4: Validate Data

4.1: Create and run a Checkpoint

Here we will create and store a CheckpointThe primary means for validating data in a production deployment of Great Expectations. for our Batch, which we can use to validate and run post-validation ActionsA Python class with a run method that takes a Validation Result and does something with it.

Checkpoints are a robust resource that can be preconfigured with a Batch Request and Expectation Suite or take them in as parameters at runtime. They can also execute numerous Actions based on the Validation Results that are returned when the Checkpoint is run.

This guide will demonstrate using a SimpleCheckpoint that takes in a Batch Request and Expectation Suite as parameters for the context.run_checkpoint(...) command.

tip

For more information on pre-configuring a Checkpoint with a Batch Request and Expectation Suite, please see our guides on Checkpoints.

4.1.1 Create a Checkpoint

We create the Checkpoint using a SimpleCheckpoint:

checkpoint = gx.checkpoint.SimpleCheckpoint(
    name="version-0.16.16 my_checkpoint",
    data_context=context,
    validations=[{"batch_request": request, "expectation_suite_name": "test_suite"}],
)

We have named the checkpoint my_checkpoint, and added one Validation, using the BatchRequest we created earlier, and our ExpectationSuite containing 2 Expectations, test_suite.

4.1.2 Run the Checkpoint

Finally, having added our Checkpoint to our Data Context, we will run the Checkpoint:

checkpoint_result = checkpoint.run()

4.2: Build and view Data Docs

Since we used a SimpleCheckpoint, our Checkpoint already contained an UpdateDataDocsAction which rendered our Data DocsHuman readable documentation generated from Great Expectations metadata detailing Expectations, Validation Results, etc. from the Validation Results we just generated. That means our Data Docs store will contain a new entry for the rendered Validation Result.

tip

For more information on Actions that Checkpoints can perform and how to add them, please see our guides on Actions.

Viewing this new entry is as simple as running:

context.open_data_docs()