What is the primary way to prevent duplicate results when performing joins in data processing?

Preventing duplicate results when performing joins in data processing is critical for ensuring the accuracy and integrity of the data. Clearing the matching fields from one of the pipelines prior to the join operation is an effective strategy. This approach helps to ensure that duplicates do not arise from overlapping data, as it removes the matching criteria from one dataset, effectively eliminating any ambiguous matches when the two datasets are joined.

When joining datasets, the presence of duplicate keys or fields can lead to a multiplication of records in the resultant dataset. By clearing the matching fields from one of the datasets involved in the join, it minimizes the chances of combining records that may otherwise incorrectly multiply due to shared values. This makes the join operation cleaner and less prone to returning repeated entries.

In contrast, other methods such as using a union operation typically combine datasets but do not specifically address duplicates stemming from joins. Similarly, implementing a coalesce strategy deals with null values rather than duplications. Creating a new derived dataset, while a viable data management practice, does not inherently solve the issue of duplicate results created during a join operation. Therefore, clearing matching fields from one of the pipelines directly targets and mitigates the risk of generating duplicate results.