With Splunk, what is the obstacle faced when implementing a schema at write?

When implementing a schema at write in Splunk, the primary obstacle is that all data must come in predefined formats. This approach requires that any incoming data conforms to a specified structure before it can be accepted into the system. This means that users must clearly define the schema upfront, which can limit the types of data that can be ingested and may lead to challenges when dealing with unstructured or semi-structured data.

This requirement can be particularly restrictive because many organizations deal with data sources that do not conform to a uniform format, which can hinder the flexibility needed for effective data integration. In contrast to schema-on-read, which allows data to be ingested in whatever format it is available and then interpreted as needed for analysis, schema at write demands a more rigid preprocessing step. This rigidity can create complications in data collection workflows, especially in environments where data formats are dynamic or evolving over time. As a result, having to manage and define all potential data formats beforehand can significantly slow down the deployment of data insights.