Data Engineer Interview | How do you handle error logging and debugging in your Python ETL ....

Question:
"How do you handle error logging and debugging in your Python ETL pipelines to ensure smooth execution?"

Answer:
"In my Python ETL pipelines, I use robust error logging with the logging module, setting up various log levels like DEBUG, INFO, WARNING, and ERROR. This helps me track issues as they occur in real-time. I also use exception handling extensively to catch specific errors and provide meaningful error messages. When using Airflow, I leverage its built-in monitoring tools to debug and trace failures quickly, ensuring the pipeline's smooth operation."