Robot Framework
Robot Framework and pytest both run Python under the hood, both have extensive library ecosystems, and both can test web apps, APIs, and more. Yet they make opposite tradeoffs, attract different users, and serve different needs. Picking the wrong one costs months of accumulated friction. This is a direct comparison
Data-Driven Testing
Data-driven testing solves a specific problem: when you have one test scenario but many input variations, writing a separate test for each variation is unsustainable. Data-driven testing lets you write the test logic once and run it against a dataset. This guide covers how data-driven testing works,
Python
pytest-BDD supports three layers of data-driven testing: Scenario Outlines in feature files, @pytest.mark.parametrize on scenario functions, and params on fixtures. Understanding when to use each — and how to combine them — is the key to eliminating test duplication without sacrificing readability. Key Takeaways * Scenario Outlines belong in
Allure
allure-pytest is the pytest plugin for Allure Report. Use @allure.step, @allure.epic, @allure.feature, and allure.attach to annotate your Python tests and generate interactive HTML reports with history, attachments, and step details. Key Takeaways Install allure-pytest and allure-commandline. The Python package generates results; the Java
Prefect
Prefect 2 has a developer-friendly testing story. Flows and tasks are plain Python functions with decorators — you can call them directly in tests, run them with local storage instead of remote infrastructure, and control state management without spinning up Prefect Cloud. This guide covers the full testing surface: task
Apache Airflow
Apache Airflow DAGs fail in production in ways that no amount of YAML linting will catch.
Testing
Syrupy is the most popular snapshot testing plugin for pytest. It stores snapshots in __snapshots__/ directories alongside your test files, uses YAML-like .ambr format for human-readable diffs, and integrates with pytest's assertion rewriting for clear failure messages. This guide covers installation, writing snapshot tests, updating snapshots,
Embedded Systems
Embedded systems testing has a reputation for being painful — manual probe sessions, proprietary test frameworks, and tests that only run on physical hardware. Python and pytest change the equation. You can write expressive, maintainable tests that run against real hardware or simulators, integrate with CI, and give embedded teams the