Skip to main content

​
Great Expectations

For Data Quality tests the open source python package Great Expectations stands out from the crowd. For those of you who don’t know, Great Expectations is a shared, open standard for data quality. It helps data teams eliminate pipeline debt, through data testing, documentation, and profiling. Learn more about the product in their documentation. With this tutorial, we show you how to configure Great Expectations to integrate with OpenMetadata and ingest your test results to your table service page.

​
Requirements

​
OpenMetadata Requirements

You will to have OpenMetadata version 0.12 or later. To deploy OpenMetadata, follow the procedure to Try OpenMetadata in Docker. Before ingesting your tests results from Great Expectations you will need to have your table metadata ingested into OpenMetadata. Follow the instruction in the Connectors section to learn more.

​
Python Requirements

You will need to install our Great Expectations submodule 
pip3 install 'openmetadata-ingestion[great-expectations]'

​
Great Expectations Setup

​
Configure your checkpoint file

Great Expectations integration leverage custom actions. To be able to execute custom actions you will need to run a checkpoint file. 
[...]
action:
  module_name: metadata.great_expectations.action
  class_name: OpenMetadataValidationAction
  config_file_path: path/to/ometa/config/file/
  database_service_name: <serviceName in OM>
  database_name: <databaseName in OM>
  schema_name: <schemaName in OM>
  table_name: <tableName in OM>
  expectation_suite_table_config_map:
    my_first_suite_name:
        database_name: <databaseName in OM>
        schema_name: <schemaName in OM>
        table_name: <tableName in OM>
    my_other_suite_name:
        database_name: <databaseName in OM>
        schema_name: <schemaName in OM>
        table_name: <tableName in OM>
[...]
In your checkpoint yaml file, you will need to add the above code block in action_list section. Properties:
  • module_name: this is OpenMetadata submodule name
  • class_name: this is the name of the class that will be used to execute the custom action
  • config_file_path: this is the path to your config.yaml file that holds the configuration of your OpenMetadata server
  • database_service_name: [Optional] this is an optional parameter. If not specified and 2 tables have the same name in 2 different OpenMetadata services, the custom action will fail
  • database_name: [Optional] The database name as it appears in OpenMetadata. For table-based validations (SqlAlchemyDatasourceBatchSpec), this is inferred from the batch spec. Required for query-based or dataframe validations (RuntimeQueryBatchSpec, RuntimeDataBatchSpec) where the table context must be explicitly specified.
  • schema_name: [Optional] The schema name as it appears in OpenMetadata. For table-based validations, this is inferred from the batch spec. Required for query-based or dataframe validations. Defaults to default if not specified.
  • table_name: [Optional] The table name as it appears in OpenMetadata. For table-based validations, this is inferred from the batch spec. Required for query-based or dataframe validations where the table cannot be automatically determined.
  • expectation_suite_table_config_map: [Optional] A dictionary mapping expectation suite names to their target OpenMetadata tables. Required when running multi-table checkpoints, where different expectation suites should send results to different tables. Each entry specifies the database_name, schema_name and table_name for routing validation results.
Multi-Table CheckpointsWhen validating multiple tables in a single checkpoint, use the expectation_suite_table_config_map parameter to route validation results to the correct OpenMetadata tables. This is necessary because:
  • Each expectation suite may target a different table
  • The checkpoint action needs to know where to send each suite’s results
  • Without the mapping, all results would attempt to go to the same default table
Example scenario: You have a checkpoint validating both users and orders tables with separate expectation suites (users_suite and orders_suite). The expectation_suite_table_config_map ensures users_suite results go to the users table and the orders_suite go to the orders table.For single-table checkpoints, this parameter is not needed - the table information is provided directly or inferred from the batch spec.
Note If you are using Great Expectation DataContext instance in Python to run your tests, you can use the run_checkpoint method as follows: 
data_context.run_checkpoint(
    checkpoint_name="my_checkpoint",
    batch_request=None,
    run_name=None,
    list_action={
        "name": "my_action_name",
        "action": {
          "module_name": "metadata.great_expectations.action",
          "class_name": "OpenMetadataValidationAction",
          "config_file_path": "path/to/ometa/config/file/",
          "database_service_name": "my_service_name",
        },
    ,}
)

​
Create your config.yaml file

To ingest Great Expectations results in OpenMetadata, you will need to specify your OpenMetadata security configuration for the REST endpoint. This configuration file needs to be located inside the config_file_path referenced in step 2 and named config.yaml. 
hostPort: http://localhost:8585/api
authProvider: azure
apiVersion: v1
securityConfig:
  clientSecret: {{ env('CLIENT_SECRET') }}
  authority: my
  clientId: 123
  scopes:
    - a
    - b
You can use environment variables in your configuration file by simply using {{ env('<MY_ENV_VAR>') }}. These will be parsed and rendered at runtime allowing you to securely create your configuration and commit it to your favorite version control tool. As we support multiple security configurations, you can check out the Enable Security section for more details on how to set the securityConfig part of the yaml file. Great Expectations config file

​
Run your Great Expectations Checkpoint File

With everything set up, it is now time to run your checkpoint file. 
great_expectations checkpoint run <my_checkpoint>
Run Great Expectations checkpoint

​
Running GX using the Python SDK?

If you are running GX using their Python SDK below is a full example of how to add the action to your code 
 import great_expectations as gx

from great_expectations.checkpoint import Checkpoint

context = gx.get_context()
conn_string = f"postgresql+psycopg2://user:pw@host:port/db"

data_source = context.sources.add_postgres(
    name="source_name",
    connection_string=conn_string,
)

data_asset = data_source.add_table_asset(
    name="name",
    table_name="table_name",
    schema_name="schema_name",
)

batch_request = data_source.get_asset("name").build_batch_request()

expectation_suite_name = "expectation_suite_name"
context.add_or_update_expectation_suite(expectation_suite_name=expectation_suite_name)
validator = context.get_validator(
    batch_request=batch_request,
    expectation_suite_name=expectation_suite_name,
)

validator.expect_column_values_to_not_be_null(column="column_name")

validator.expect_column_values_to_be_between(
    column="column_name", min_value=min_value, max_value=max_value
)

validator.save_expectation_suite(discard_failed_expectations=False)

my_checkpoint_name = "my_sql_checkpoint"

checkpoint = Checkpoint(
    name=my_checkpoint_name,
    run_name_template="%Y%m%d-%H%M%S-my-run-name-template",
    data_context=context,
    batch_request=batch_request,
    expectation_suite_name=expectation_suite_name,
    action_list=[
        {
            "name": "store results in OpenMetadata",
            "action": {
                "module_name": "metadata.great_expectations.action",
                "class_name": "OpenMetadataValidationAction",
                "config_file_path": "path/to/config/file",
                "database_service_name": "openmetadata_service_name",
                "database_name": "database_name",
                "table_name": "table_name",
                "schema_name": "schema_name",
            }
        },
    ],
)

context.add_or_update_checkpoint(checkpoint=checkpoint)
checkpoint_result = checkpoint.run()

​
Multi-Table Checkpoint Example

Validate multiple tables in a single checkpoint run: 
import great_expectations as gx
from great_expectations.checkpoint import Checkpoint

context = gx.get_context()
conn_string = "postgresql+psycopg2://user:pw@host:port/db"

data_source = context.sources.add_postgres(
  name="my_datasource",
  connection_string=conn_string,
)

# Set up users table validation
users_asset = data_source.add_table_asset(
  name="users_asset",
  table_name="users",
  schema_name="public",
)
users_suite = "users_suite"
context.add_or_update_expectation_suite(expectation_suite_name=users_suite)
users_validator = context.get_validator(
  batch_request=users_asset.build_batch_request(),
  expectation_suite_name=users_suite,
)
users_validator.expect_column_values_to_not_be_null(column="email")
users_validator.save_expectation_suite(discard_failed_expectations=False)

# Set up orders table validation
orders_asset = data_source.add_table_asset(
  name="orders_asset",
  table_name="orders",
  schema_name="public",
)
orders_suite = "orders_suite"
context.add_or_update_expectation_suite(expectation_suite_name=orders_suite)
orders_validator = context.get_validator(
  batch_request=orders_asset.build_batch_request(),
  expectation_suite_name=orders_suite,
)
orders_validator.expect_column_values_to_be_between(column="amount", min_value=0, max_value=1000000)
orders_validator.save_expectation_suite(discard_failed_expectations=False)

# Create multi-table checkpoint
checkpoint = Checkpoint(
  name="multi_table_checkpoint",
  run_name_template="%Y%m%d-%H%M%S-multi-table",
  data_context=context,
  validations=[
      {
          "batch_request": users_asset.build_batch_request(),
          "expectation_suite_name": users_suite,
      },
      {
          "batch_request": orders_asset.build_batch_request(),
          "expectation_suite_name": orders_suite,
      },
  ],
  action_list=[
      {
          "name": "openmetadata_action",
          "action": {
              "module_name": "metadata.great_expectations.action",
              "class_name": "OpenMetadataValidationAction",
              "config_file_path": "/path/to/config/",
              "database_service_name": "my_postgres_service",
              "expectation_suite_table_config_map": {
                  "users_suite": {
                      "database_name": "production",
                      "schema_name": "public",
                      "table_name": "users",
                  },
                  "orders_suite": {
                      "database_name": "production",
                      "schema_name": "public",
                      "table_name": "orders",
                  },
              },
          }
      },
  ],
)

context.add_or_update_checkpoint(checkpoint=checkpoint)
checkpoint_result = checkpoint.run()

​
Working with GX 1.x.x?

In v1.x.x GX introduced significant changes to their SDK. One notable change was the removal of the great_expectations CLI. OpenMetadata introduced support for 1.x.x version through its OpenMetadataValidationAction1xx class. You will need to first `pip install ‘open-metadata[great-expectations-1xx]’. Below is a complete example 
import great_expectations as gx

from metadata.great_expectations.action1xx import OpenMetadataValidationAction1xx # OpenMetadata Validation Action for GX 1.x.x


context = gx.get_context()
conn_string = f"redshift+psycopg2://user:pw@host:port/db"

data_source = context.data_sources.add_redshift(
    name="name",
    connection_string=conn_string,
)

data_asset = data_source.add_table_asset(
    name="name",
    table_name="table_name",
    schema_name="schema_name",
)

batch_definition = data_asset.add_batch_definition_whole_table("batch definition")
batch = batch_definition.get_batch()

suite = context.suites.add(
    gx.core.expectation_suite.ExpectationSuite(name="name")
)
suite.add_expectation(
    gx.expectations.ExpectColumnValuesToNotBeNull(
        column="column_name"
    )
)
suite.add_expectation(
    gx.expectations.ExpectColumnValuesToBeBetween(column="column_name", min_value=50, max_value=1000000)
)

validation_definition = context.validation_definitions.add(
    gx.core.validation_definition.ValidationDefinition(
        name="validation definition",
        data=batch_definition,
        suite=suite,
    )
)

action_list = [
    OpenMetadataValidationAction1xx(
        database_service_name="openmetadata_service_name",
        database_name="openmetadata_database_name",
        table_name="openmetadata_table_name",
        schema_name="openmetadata_schema_name",
        config_file_path="path/to/config/file",
    )
]

checkpoint = context.checkpoints.add(
    gx.checkpoint.checkpoint.Checkpoint(
        name="checkpoint", validation_definitions=[validation_definition], actions=action_list
    )
)

checkpoint_result = checkpoint.run()

​
List of Great Expectations Supported Test

We currently only support a certain number of Great Expectations tests. The full list can be found in the Tests section. If a test is not supported, there is no need to worry about the execution of your Great Expectations test. We will simply skip the tests that are not supported and continue the execution of your test suite.