Which index type typically handles unstructured data?

The index type that typically handles unstructured data is the event index. This is primarily because unstructured data can take various forms, such as logs, documents, and other types of records that do not have a predefined data model. Event indexes in Splunk are specifically designed to ingest, index, and make searchable large volumes of unstructured data, allowing users to extract meaningful information from this data using searches and analysis.

Event indexes provide the flexibility to work with different formats and types of data as they are built to accommodate the characteristics of log data, which is often non-relational and variable. They also support timestamping, making it easy to perform time-based searches and analysis, which is a critical aspect of working with unstructured data in the context of monitoring and troubleshooting systems.

In contrast, other index types like metrics are tailored for structured data that represents numeric values over time, time-series can aggregate metrics data in a time-based manner, and summary indexes store search results from real-time or historical searches but do not typically deal with the raw unstructured data itself. Thus, the event index is the most suitable choice for managing unstructured data within Splunk.