How to use Great Expectations with Amazon Web Services using Athena

In progress

Great Expectations is aware that the fluent datasource and yaml config workflows are undocumented. A new method for connecting to data sources will be added to this topic soon. In the meantime, use the Great Expectations Slack channel if you encounter issues and need assistance.

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 stored in an Athena 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

1.3 Create your Data Context

For purposes of this example, we will assume that we have an empty folder to initialize our Filesystem Data Context in:

Python code

path_to_empty_folder = '/my_gx_project/'

We will provide our empty folder's path to the GX library's FileDataContext.create(...) method as the project_root_dir parameter. Because we are providing a path to an empty folder FileDataContext.create(...) will initialize a Filesystem Data Context at that location.

For convenience, the FileDataContext.create(...) method will then instantiate and return the newly initialized Data Context, which we can keep in a Python variable.

Python code

from great_expectations.data_context import FileDataContext

context = FileDataContext.create(project_root_dir=path_to_empty_folder)

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 Verify that the new Amazon S3 Expectations Store has been added successfully

You can verify that your Stores are properly configured by running the command:

Terminal command

great_expectations store list

This will list the currently configured Stores that Great Expectations has access to. If you added a new S3 Expectations Store, the output should include the following ExpectationsStore entry:

Terminal output

- name: expectations_S3_store
class_name: ExpectationsStore
store_backend:
  class_name: TupleS3StoreBackend
  bucket: '<your_s3_bucket_name>'
  prefix: '<your_s3_bucket_folder_name>'

Notice the output contains only one Expectation Store: your configuration contains the original expectations_store on the local filesystem and the expectations_S3_store we just configured, but the great_expectations store list command only lists your active stores. For your Expectation Store, this is the one that you set as the value of the expectations_store_name variable in the configuration file: expectations_S3_store.

1.4.4 (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

1.4.5 (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 Verify that the new Amazon S3 Validation Results Store has been added successfully

To make Great Expectations look for Validation Results in an S3 bucket, set the validations_store_name variable in great_expectations.yml to the name of your S3 Validations Store as shown in the following example:

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>'

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

1.5.4 (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 Choose how to run the code for creating a new Datasource

The previous sections of this guide involved manually editing configuration files to add configurations for Amazon S3 buckets. When setting up your Datasource configurations, it is simpler to use Great Expectation's Python API. We recommend doing this from a Jupyter Notebook, as you will then receive immediate feedback on the results of your code blocks. However, you can alternatively use a Python script in the IDE of your choice.

If you would like, you may use the Great Expectations CLICommand Line Interface to automatically generate a pre-configured Jupyter Notebook. To do so, run the following console command from the root directory of your Great Expectations project:

great_expectations datasource new

Once you have your pre-configured Jupyter Notebook, you should follow along in the YAML-based workflow in the following steps.

If you choose to work from a blank Jupyter Notebook or a Python script, you may find it easier to use the following Python dictionary workflow over the YAML workflow. Great Expectations supports either configuration method.

2.2 Instantiate your project's DataContext

Import these necessary packages and modules.

import great_expectations as gx
from great_expectations.core.batch import Batch, BatchRequest, RuntimeBatchRequest
from great_expectations.core.yaml_handler import YAMLHandler

yaml = YAMLHandler()

Load your DataContext into memory using the get_context() method.

context = gx.get_context()

2.3 Configure your Datasource

2.3.1 Determine your connection string

In order for Great Expectations to connect to Athena, you will need to provide a connection string. To determine your connection string, reference the examples below and the PyAthena documentation.

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.

The following urls don't include credentials as it is recommended to use either the instance profile or the boto3 configuration file.

If you want Great Expectations to connect to your Athena instance (without specifying a particular database), the URL should be:

awsathena+rest://@athena.{region}.amazonaws.com/?s3_staging_dir={s3_path}

Note the url parameter "s3_staging_dir" needed for storing query results in S3.

If you want Great Expectations to connect to a particular database inside your Athena, the URL should be:

awsathena+rest://@athena.{region}.amazonaws.com/{database}?s3_staging_dir={s3_path}

Tip: Using credentials instead of connection_string

The credentials key uses a dictionary to provide the elements of your connection string as separate, individual values. For information on how to populate the credentials dictionary and how to configure your great_expectations.yml project config file to populate credentials from either a YAML file or a secret manager, please see our guide on How to configure credentials.

2.3.2 Create your Datasource configuration

Using the following example configuration, add in the CONNECTION_STRING for your database:

• YAML
• Python

datasource_yaml = f"""
name: my_awsathena_datasource
class_name: Datasource
execution_engine:
  class_name: SqlAlchemyExecutionEngine
  module_name: great_expectations.execution_engine
  connection_string: {connection_string}
data_connectors:
  default_runtime_data_connector_name:
    class_name: RuntimeDataConnector
    batch_identifiers:
      - default_identifier_name
    module_name: great_expectations.datasource.data_connector
  default_inferred_data_connector_name:
    class_name: InferredAssetSqlDataConnector
    module_name: great_expectations.datasource.data_connector
    include_schema_name: true
"""

datasource_dict = {
    "name": "my_awsathena_datasource",
    "class_name": "Datasource",
    "execution_engine": {
        "class_name": "SqlAlchemyExecutionEngine",
        "module_name": "great_expectations.execution_engine",
        "connection_string": connection_string,
    },
    "data_connectors": {
        "default_runtime_data_connector_name": {
            "class_name": "RuntimeDataConnector",
            "batch_identifiers": ["default_identifier_name"],
            "module_name": "great_expectations.datasource.data_connector",
        },
        "default_inferred_data_connector_name": {
            "class_name": "InferredAssetSqlDataConnector",
            "module_name": "great_expectations.datasource.data_connector",
            "include_schema_name": "true",
        },
    },
}

To configure a SQL datasource such as the one used to connect to your Athena data, see How to configure a SQL Datasource

2.4 Save the Datasource configuration to your DataContext

Save the configuration into your DataContext by using the add_datasource() function.

• YAML
• Python

context.add_datasource(**yaml.load(datasource_yaml))

context.add_datasource(**datasource_config)

2.5 Test your new Datasource

First, create a Batch Request.

batch_request = {
    "datasource_name": "my_awsathena_datasource",
    "data_connector_name": "default_inferred_data_connector_name",
    "data_asset_name": f"{ATHENA_DB_NAME}.taxitable",
    "limit": 1000,
}

Next, prepare an empty Expectation suite.

expectation_suite_name = "version-0.16.16 my_awsathena_expectation_suite"
try:
    suite = context.get_expectation_suite(expectation_suite_name=expectation_suite_name)
    print(
        f'Loaded ExpectationSuite "{suite.expectation_suite_name}" containing {len(suite.expectations)} expectations.'
    )
except DataContextError:
    suite = context.add_expectation_suite(expectation_suite_name=expectation_suite_name)
    print(f'Created ExpectationSuite "{suite.expectation_suite_name}".')

Now you can load data into a Validator. If this is successful then you will have verified that your Datasource is working properly.

validator = context.get_validator(
    batch_request=BatchRequest(**batch_request),
    expectation_suite_name=expectation_suite_name,
)
validator.head(n_rows=5, fetch_all=False)

Part 3: Create Expectations

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

When we tested our Datasource in step 2.5: 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

First we create the Checkpoint configuration:

• YAML
• Python

my_checkpoint_name = "version-0.16.16 insert_your_checkpoint_name_here"
my_checkpoint_config = f"""
name: {my_checkpoint_name}
config_version: 1.0
class_name: SimpleCheckpoint
run_name_template: "%Y%m%d-%H%M%S-my-run-name-template"
"""

my_checkpoint_name = "version-0.16.16 my_databricks_checkpoint"

checkpoint = SimpleCheckpoint(
    name=my_checkpoint_name,
    config_version=1.0,
    class_name="version-0.16.16 SimpleCheckpoint",
    run_name_template="%Y%m%d-%H%M%S-my-run-name-template",
    data_context=context,
)

Once we have defined our Checkpoint configuration, we can test our syntax using context.test_yaml_config(...):

• YAML

my_checkpoint = context.test_yaml_config(my_checkpoint_config)

Note that we get a message that the Checkpoint contains no validations. This is OK because we will pass them in at runtime, as we can see below when we call context.run_checkpoint(...).

4.1.2 Save the Checkpoint

We can now add the Checkpoint to our Data Context:

    context.add_or_update_checkpoint(checkpoint)

4.1.3 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()

Congratulations!

🚀🚀 Congratulations! 🚀🚀 You have successfully navigated the entire workflow for using Great Expectations with Amazon Web Services and Athena, from installing Great Expectations through Validating your Data.