For example, using the CLI to add a Postgres backend.
All that’s needed to get the Operator to point at an external dataset is to set up an Airflow Connection to the datasource, and add the connection to your Great Expectations project. The GreatExpectationsOperator can run a checkpoint on a dataset stored in any backend compatible with Great Expectations.
Airflow tutorial full#
A checkpoint_config may be passed to the operator in place of a name, and can be defined like this example.įor a full list of parameters, see the GreatExpectationsOperator documentation. With a checkpoint_name, checkpoint_kwargs may be passed to the operator to specify additional, overwriting configurations. If using an in-memory data_context_config, a DataContextConfig must be defined, as in this example.Ī checkpoint_name references a checkpoint in the project CheckpointStore defined in the DataContext (which is often the great_expectations/checkpoints/ path), so that a checkpoint_name = "" would reference the file great_expectations/checkpoints/taxi/pass/chk.yml. The data_context_root_dir should point to the great_expectations project directory generated when you created the project with the CLI. The operator has several optional parameters, but it always requires either a data_context_root_dir or a data_context_config and either a checkpoint_name or checkpoint_config.
Airflow tutorial install#
To import the GreatExpectationsOperator in your Airflow project, run the following command to install the Great Expectations provider in your Airflow environment: It organizes storage and access for Expectation Suites, Datasources, notification settings, and data fixtures.Ĭheckpoints provide a convenient abstraction for bundling the validation of a Batch (or Batches) of data against an Expectation Suite (or several), as well as the actions that should be taken after the validation. See here for the guide on using Great Expectations with Airflow from within Astronomer.īefore you start writing your DAG, you will want to make sure you have a Data Context and Checkpoint configured.Ī Data Context represents a Great Expectations project. This guide focuses on using Great Expectations with Airflow in a self-hosted environment.
Airflow tutorial how to#
This document explains how to use the GreatExpectationsOperator to perform data quality work in an Airflow DAG. DAGs complete work through operators, which are templates that each encapsulate a specific type of work. Created a checkpoint for that Expectation Suite and a data assetĪirflow is a data orchestration tool for creating and maintaining data pipelines through DAGs (directed acyclic graphs) written in Python.Set up a working deployment of Great Expectations.
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