kedro-great 0.2.7

Kedro Great makes integrating Great Expectations with Kedro easy!

Kedro Great

As Seen on DataEngineerOne
Watch the Video: Kedro Great: Use Great Expectations with Ease!

Kedro Great is an easy-to-use plugin for kedro that makes integration with Great Expectations fast and simple.

Hold yourself accountable to Great Expectations.
Never have fear of data silently changing ever again.

Quick Start

Install

Kedro Great is available on pypi, and is installed with kedro hooks.

pip install kedro-great

Setup

Once installed, kedro great becomes available as a kedro command.

You can use kedro great init to initialize a Great Expectations project, and then automatically generate its project context.

Furthermore, by using kedro great init, you also generate Great Expectations Datasources and Suites to use with your catalog.yml DataSets.

By default, expectation suites are named for the catalog.yml name and a basic.json is generated for each.

kedro great init

Use

After the Great Expectations project has been setup and configured, you can now use the KedroGreat hook to run all your data validations every time the pipeline runs.

# run.py
from kedro_great import KedroGreat

class ProjectContext(KedroContext):
    hooks = (
        KedroGreat(),
    )

Then just run the kedro pipeline to run the suites.

kedro run

Results

Finally, you can use great_expectations itself to generate documentation and view the results of your pipeline.

Love seeing those green ticks!

great_expectations docs build

Hook Options

The KedroGreat hook supports a few options currently. If you wish to

expectations_map: Dict[str, Union[str, List[str]]]

If you have multiple expectation suites you wish to run, or expectation suites that do not have the same name as the catalog dataset, these mappings can be specified in the expectations_map argument for KedroGreat

Default: The catalog name is the expectation name.

Note: Specifying a suite type such as .basic will override all other suite types

KedroGreat(expectations_map={
    'pandas_iris_data': 'pandas_iris_data',
    'spark_iris_data': ['spark_iris_data',
                        'other_expectation',
                        'another_expectation.basic'],

})

suite_types: List[Optional[str]]

If your suites have multiple types, you can choose exactly which types to run.

A None means that a suite will not have the type appended to the name.

Default: The KedroGreat.DEFAULT_SUITE_TYPES.

Node: If a suite type is already specified in the expectations_map, that will override this list.

KedroGreat(suite_types=[
    'warning',
    'basic',
    None
])

run_before_node:bool, run_after_node: bool

You can decide when the suites run, before or after a node or both before and after a node.

It will operate on the node inputs and outputs respectively.

Default: Only runs before a node runs.

KedroGreat(run_before_node=True, run_after_node=False)

fail_fast: bool, fail_after_pipeline_run: bool

You can also have KedroGreat throw a SuiteValidationFailure when a Great Expectations validation fails.

Either the exception can be throw immediately, or the exceptions can be aggregated over the whole pipeline run, and thrown at the end.

This is useful for when you wish to run validation on your pipeline in a CI/CD way.

Default: Neither are set

KedroGreat(fail_fast=True, fail_after_pipeline_run=True)