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You can use Python dataclasses with Python match statements to create cleaner and more readable code. This approach can be particularly useful when setting conditions based on multiple attributes of a class.
Timely detection and notification of data anomalies are crucial for stakeholders to address potential issues promptly.
Kestra, an open-source orchestrator, simplifies this process by enabling you to create a workflow using a YAML file.
In the given example, a DuckDB query is used to identify anomalies, and if any are detected, an email with the anomalous rows in a CSV file is sent to relevant parties.
pytest.mark lets you label test functions for conditional or selective execution based on specific needs.
For instance, you can mark slow tests or tests involving integration with external services to run them separately or exclude them from regular test runs. This helps you organize and execute your tests more effectively.
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