Waiting on an externally, asynchronously generated file before continuing is a common scenario in Airflow.
But “I’ll wait as long as it takes” is a heavy promise to make on your infrastructure’s behalf.

When tasks depend on each other yet remain independent events, what is the most elegant way to handle them?
Should we use a loop? Add another DAG? Or is there a better solution?

When building dependencies between DAGs, you don’t always have to rely on RESTful APIs.
Airflow also provides a built-in Operator for this purpose.

Pyenv is a tool for managing different Python versions.
Whether you’re upgrading a project to a newer Python release or switching between projects with different requirements, pyenv makes the process simple and hassle-free.

Introducing the factory pattern in your code can significantly improve its structure—but it also makes unit testing a bit trickier.
We’ll walk through two different mocking strategies, compare their pros and cons, and help you write more robust tests!

With mock objects, we can simulate database operations without actually connecting to a real database.
This means we don’t need to prepare a dedicated test database or worry about cleaning it up after tests.

After covering the basic tools, this article demonstrates how to use mocks effectively in Pytest.
Mocks allow us to replace the logic of real functions to simulate different scenarios and write more precise unit tests.
We’ll walk through examples of mocking functions, simulating errors, and even handling external dependencies like Redis.