Is it considered best practice to modify pre-trained Splunk sourcetypes instead of creating custom sourcetypes?

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Modifying pre-trained Splunk sourcetypes is generally not considered best practice because it can lead to complications and inconsistencies in data management. Pre-trained sourcetypes are designed to be used as they are, offering a baseline for data interpretation that has been rigorously tested and optimized. By creating custom sourcetypes, administrators have the flexibility to tailor the data parsing and indexing to fit specific requirements without risking the integrity of default configurations.

Creating custom sourcetypes allows for better organization, clarity, and enhanced control over how data is handled. It enables practitioners to define unique parsing rules, timestamps, and field extractions that are specific to their datasets, which can improve search performance and accuracy. This approach contributes to a more maintainable and coherent environment, ensuring that future changes or upgrades won't disrupt existing workflows.

In scenarios where small datasets are involved, or depending on particular use cases, there may be some rationale for adjusting existing sourcetypes. However, the more sustainable and effective strategy is to develop custom sourcetypes that align closely with the unique characteristics of the data being ingested.

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