Great Expectations

Great Expectations is a Python-based open-source library for validating, documenting, and profiling your data. It helps to maintain data quality and improve communication about data between teams.

The goodness of data validation in Great Expectations can be integrated with Flyte to validate the data moving in and out of the pipeline entities you may have defined in Flyte. This helps establish stricter boundaries around your data to ensure that everything is as you expected and data will not crash your pipelines anymore unexpectedly!

How to Define Your Integration

Great Expectations supports native execution of expectations against various Datasources, such as Pandas dataframes, Spark dataframes, and SQL databases via SQLAlchemy.

We’re supporting two Flyte types that should suit Great Expectations’ Datasources:

  • flytekit.types.file.FlyteFile: FlyteFile represents an automatic persistence object in Flyte. It can represent files in remote storage and Flyte will transparently materialize them in every task execution.

  • flytekit.types.schema.FlyteSchema: FlyteSchema supports tabular data, which the plugin will convert into a parquet file and validate the data using Great Expectations.

Note

Flyte types are added because, in Great Expectations, we have the privilege to give a non-string (Pandas/Spark DataFrame) when using a RuntimeDataConnector but not when using an InferredAssetFilesystemDataConnector or a ConfiguredAssetFilesystemDataConnector. For the latter case, with the integration of Flyte types, we can give a Pandas/Spark DataFrame or a remote URI as the dataset.

The datasources can be well-integrated with the plugin using the following two modes:

  • Flyte Task: A Flyte task defines the task prototype that one could use within a task or a workflow to validate data using Great Expectations.

  • Flyte Type: A Flyte type helps attach the GreatExpectationsType to any dataset. Under the hood, GreatExpectationsType can be assumed as a combination of Great Expectations and Flyte types where every bit of data is validated against the expectations, much like the OpenAPI Spec or the gRPC validator.

You can see some nice examples in the Python code files.

Data Validation Failure

If the data validation fails, the plugin will raise a Great Expectations’ ValidationError.

For example, this is how the error message looks like:

Traceback (most recent call last):
...
great_expectations.marshmallow__shade.exceptions.ValidationError: Validation failed!
COLUMN          FAILED EXPECTATION
passenger_count -> expect_column_min_to_be_between
passenger_count -> expect_column_mean_to_be_between
passenger_count -> expect_column_quantile_values_to_be_between
passenger_count -> expect_column_values_to_be_in_set
passenger_count -> expect_column_proportion_of_unique_values_to_be_between
trip_distance -> expect_column_max_to_be_between
trip_distance -> expect_column_mean_to_be_between
trip_distance -> expect_column_median_to_be_between
trip_distance -> expect_column_quantile_values_to_be_between
trip_distance -> expect_column_proportion_of_unique_values_to_be_between
rate_code_id -> expect_column_max_to_be_between
rate_code_id -> expect_column_mean_to_be_between
rate_code_id -> expect_column_proportion_of_unique_values_to_be_between

Plugin Parameters

  • datasource_name: Data source, in general, is the “name” we use in the Great Expectations config file. A Datasource brings together a way of interacting with data (like a database or Spark cluster) and some specific data (like a CSV file, or a database table). Moreover, data source assists in building batches out of data (for validation).

  • expectation_suite_name: Defines the data validation.

  • data_connector_name: Tells how the data batches have to be identified.

Optional Parameters

  • context_root_dir: Sets the path of the great expectations config directory.

  • checkpoint_params: Optional great_expectations.checkpoint.checkpoint.SimpleCheckpoint class parameters.

  • batch_request_config: Additional batch request configuration parameters.

    • data_connector_query: Query to request a data batch

    • runtime_parameters: Parameters to be sent at run-time

    • batch_identifiers: Batch identifiers

    • batch_spec_passthrough: Reader method if your file doesn’t have an extension

  • data_asset_name: Name of the data asset (to be used for RuntimeBatchRequest)

  • local_file_path: Helpful to download the given dataset to the user-given path

Note

You may always want to mention the context_root_dir parameter, as providing a path means no harm! Moreover, local_file_path is essential when using FlyteFile and FlyteSchema.

Plugin Installation

To use the Great Expectations <> Flyte plugin, run the following command:

pip install flytekitplugins-great_expectations

Note

Make sure to run the workflows in the “flytekit_plugins” directory, both locally and within the sandbox.

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