What is the best way to handle errors during a data conversion process?

Handling errors during a data conversion process is crucial to ensure data integrity and reliability. Wrap the data conversion code in try-catch blocks to catch and handle any exceptions that occur during the conversion process. This allows you to gracefully handle errors without crashing the entire program. Implement logging mechanisms to record errors encountered during the data conversion. Logging helps in tracking and troubleshooting issues later on. Include relevant information such as the error message, timestamp, and the data being processed. Provide meaningful error messages to users or system administrators when errors occur.

Here are some of the ways to handle errors during data conversion, 1.Detect errors early with thorough validation. 2.Log errors and provide detailed reports. 3.Handle errors gracefully with try-catch blocks. 4.Resolve errors automatically or with manual intervention. 5.Implement rollback mechanisms for data recovery. 6.Keep users informed about the process and errors. 7.Document common errors and provide training. 8.Continuously improve error handling based on feedback.

Algunos observaciones a tener en cuenta sobre las estrategias: > Siempre los datos originales nunca se modifican: siempre transformalos y guarda las transformaciones en otro espacio. > La estrategia "rechazar" es sobre todo el registro; y no solo un grupo de campos en especifico. Esto debes guardarlo en un espacio aparte. > La estrategia "ignorar" funciona para campos con menor o ninguna cantidad de reglas sobre la dimensión validación e integridad. > La estrategia "corregir" requiere no saber de antemano cuales pueden ser los errores para aplicar una logica de correción, sino estaria dentro de la logica de conversión en primer lugar. > La estrategía "corregir" requiere separar los datos para analizarlos y plantear una solución.

1. Some errors could be caused by intermittent issues and unrelated to the transformation. There should be a backoff retry logic to handle such errors. 2. Some errors are related to upstream data structure deviating from predefined version. Such errors should be raised, and could be redirected to a different place (ex: DLQ for SQS, Error directory for kinesis etc.) for further manual checks if needed. 3. Some errors can help decide the evaluation of logic going forward (if else) 4. Some errors could be based on UI trying to load incorrect data into a table, where a trigger might raise exception after data validation. There are many other scenarios not covered in above examples, but fall under at least one of ignore, reject, or correct.

Ignore: Accept data as-is without taking corrective action; suitable for minor errors. Reject: Discard data containing major errors; appropriate for irreparable errors. Correct: Modify data to fix errors; suitable for moderate, solvable errors via rules or transformations.

Utilize try-except blocks in Python for catching and handling exceptions. Use error output configurations in SSIS to redirect or log error rows. Employ IF-THEN-ELSE statements in SQL to check and correct error conditions. Implement custom functions or scripts in any tool to perform error handling logic.

Error Handling Techniques 1. Try-Except statement 2. Raise statement 3. Execute Error class 4. Traceback 5. Result Object Error messages

Try-catch blocks to capture errors as they occur. Using error codes and messages to inform users and developers about the nature of the problem. Implementing fallback procedures , such as logging and user notifications, to minimize disruption. Input validation to prevent errors before they happen.