How can invalid data be handled in a Databricks pipeline?

Implementing validation checks is an effective way to handle invalid data in a Databricks pipeline. Validation checks ensure that the data meets specific criteria and standards before it is processed further. By defining rules and constraints, you can identify and address issues such as missing values, incorrect formats, or out-of-range entries. This proactive approach allows you to either correct the identified invalid data, flag it for review, or exclude it from further analysis, thereby maintaining the integrity and quality of the overall dataset.

In contrast, simply ignoring invalid data can lead to incomplete or misleading analyses, as important insights may be overlooked. Duplicating erroneous records does not solve any problems; it only complicates the dataset and could lead to confusion in the analyses. Archiving all data, while maintaining a record of it, does not effectively address or resolve the issues present in the invalid data. Therefore, implementing validation checks provides a practical and systematic approach to ensure that data integrity is upheld in the pipeline.