When Union-ing two datasets with a slightly different schema, what should you be cautious of?

When union-ing two datasets with a slightly different schema, it is essential to be cautious about data types. In a union operation, the datasets being combined must have compatible data types for corresponding fields. If the fields have different data types, it can lead to errors or unexpected results, such as data loss or misinterpretation of the data.

For example, if one dataset has a numeric field and the other has it defined as a string, the union operation may not know how to properly combine these fields, which can result in incorrect outputs or failures in processing the data. Therefore, ensuring that the data types are consistent between the two datasets is critical for a successful union operation.

While considerations like data duplication, field name length, and row order are important aspects to keep in mind, they do not directly affect the compatibility of the fields in terms of combining data through a union. Ensuring that the data types align is crucial for maintaining data integrity and achieving the desired results in the combined dataset.