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Using a defined process to capture and analyze erroneous data is considered the best practice for handling dead-letter queues in a Dataflow pipeline. This approach ensures that any data that cannot be processed successfully is systematically collected and analyzed, allowing for identification of root causes and implementation of necessary corrections.

By capturing and analyzing the erroneous data, you gain insights into the nature of the failures, whether they are due to bad data, unexpected formats, or other issues. This promotes a cycle of continuous improvement, whereby the pipeline can be adjusted to better handle similar data in the future. Furthermore, this process minimizes disruption to the main data flow by allowing the system to complete processing successfully while still providing a clear pathway to address the errors outside the regular flow.

In contrast, manually rerunning failed tasks could lead to inefficiencies and is not scalable. Logging the errors for review is useful but can often leave gaps in understanding the context or underlying issues, hence merely documenting errors without a proactive analysis may not lead to effective resolution. Preventing errors from reaching the pipeline is an ideal scenario; however, it is often impractical to eliminate every potential error at the outset, and therefore having a defined process for handling errors becomes essential.