Ensuring Data Pipeline Integrity: A Comprehensive Guide to Testing Airflow DAGs

  • Manmeet Kaur Rangoola

Testing Airflow DAGs is crucial to ensure error-free, reliable, and performant data pipelines. To achieve this, understanding why tests are needed, where they fit in the data pipeline, and where to implement them in your data pipelines is essential. Let’s explore these questions in the context of a data pipeline and then proceed with an example implementation.

This blog post aims to present real-world scenarios and introduce different types of tests that can be used in your data pipeline. Based on your use case, you might choose to implement either all types of tests or only a selected few.

Why include tests in a data pipeline

Consider a data pipeline that reads the data from an S3 bucket, loads it into a Redshift stage table and then upsert the data to the final target table. Assume that this DAG runs on a daily schedule.

Now, try to think of what all could go wrong with this pipeline:

To allow our data pipelines to adapt and scale with our data ecosystem, our code should adhere to standards for input, output, and processing. Adding appropriate unit tests to your code will help you alleviate not only basic programming errors but also the business errors they might cause downstream. In my experience, this is the area where many data engineering teams falter; in trying to be agile and meet deadlines, the focus is on delivery, not on testing. Hence, at times, they incur more tech debt by not writing appropriate unit tests or data quality tests.

Basic tests for a data pipeline

The scenarios we discussed in the previous section can be handled gracefully by incorporating the following tests:

The tests we discuss here are in no way an exhaustive list of different types of tests, but some of the commonly used ones. As a team moves forward in their data journey, the complexity of these tests will increase along with number of different types of tests.

Where should I include tests in a data pipeline

Enforce checks using Airflow Policies

In some cases, Airflow administrators want to implement controls to ensure that DAGs meet certain standards across the organization or teams. They prefer to centralize these controls for quality assurance. Airflow Cluster Policies can be used for this purpose. These policies are not tests but checks that allows you to enforce quality standards. This approach enables users to avoid duplicating certain basic tests or checks for each Airflow project separately. Additionally, for administrators, it provides a central location to manage and enforce these standards.

Cluster policies are a set of functions that Airflow administrators can define in their airflow_local_settings module or using the pluggy interface to mutate or perform custom logic on a few important Airflow objects like DAG, Task, Task Instance etc. For example, you could:

Watch this deep-dive video into cluster policies by Philippe Gagnon.


The tests we discussed here are just a subset of a larger set that we can implement to make more robust and stable pipelines. However, this subset is good enough to get started with if you are new to testing in Airflow or any other data pipelines. Beyond this, there are advanced tests that you can implement, like performance tests, optimization tests, scalability tests, end-to-end system tests, security tests, and so on. We can dive deeper into these as well in future blogs.

In conclusion, data validation tests, unit tests, and data quality checks play vital roles in ensuring the reliability, accuracy, and integrity of data pipelines and hence, your data that powers your business. These checks ensure that while you quickly build data pipelines to meet your deadlines, they are actively catching errors, improving development times, and reducing unforeseen errors in the background. Astro CLI plays an important part in this by providing commands like astro dev parse and astro dev pytest to integrate tests seamlessly. Additionally, it also gives you the option to test your Airflow project when upgrading to a higher version for Python dependencies and DAGs against the new version of Airflow.

Start optimizing your Airflow projects today with Astro CLI—your gateway to robust, error-free data pipelines. Also, check out the detailed documentation on testing Airflow DAGs in the Astronomer docs.

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