# ask-ai

Greg Linklater

02/07/2024, 2:00 PM
@Glime can you give me an example of using Great Expectations in a Flyte Workflow?


02/07/2024, 2:01 PM
To use Great Expectations within a Flyte workflow, you can leverage the integration between Flyte and Great Expectations to validate data moving in and out of the pipeline entities defined in Flyte. This integration helps in making the Flyte pipelines more robust and resilient by enforcing validation rules on the data, eliminating bad data, preventing unexpected data-related crashes, and ensuring data quality. Here's a simplified example of how you might define a task in Flyte that uses Great Expectations for data validation:
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from flytekit import task, workflow from flytekitplugins.great_expectations import BatchRequestConfig, GreatExpectationsTask from pandas import DataFrame  # Define a Great Expectations task @task def my_ge_task(dataframe: DataFrame) -> DataFrame:     # Define the batch request configuration     batch_request_config = BatchRequestConfig(         datasource_name="my_datasource",         data_connector_name="my_data_connector",         data_asset_name="my_data_asset",         batch_identifiers={"default_identifier_name": "default_identifier_value"},     )      # Define the Great Expectations task     ge_task = GreatExpectationsTask(         name="great_expectations_task",         inputs={"dataframe": DataFrame},         expectation_suite_name="my_expectation_suite",         data_context_root_dir="my_data_context_root_dir",         batch_request_config=batch_request_config,     )      # Execute the Great Expectations task     validated_df = ge_task(dataframe=dataframe)     return validated_df  # Define a workflow that uses the Great Expectations task @workflow def my_workflow(input_df: DataFrame) -> DataFrame:     validated_df = my_ge_task(dataframe=input_df)     return validated_df
In this example,
is a task that uses Great Expectations to validate a
. The
is configured with necessary parameters such as the expectation suite name, data context root directory, and batch request configuration. This task can then be used within a Flyte workflow (
) to ensure that the data being processed meets the defined expectations. Please note, this is a simplified example to illustrate the concept. The actual implementation may vary based on your specific requirements, such as the data source, data connectors, and expectation suites you are using with Great Expectations. For more detailed information on integrating Great Expectations with Flyte, including setting up data sources and expectation suites, refer to the Flyte documentation and Great Expectations documentation. ###S