What is one of the main functionalities of Data Model Acceleration in Splunk?

Data Model Acceleration in Splunk primarily serves to enhance the performance of searches that involve multiple data models by allowing for faster investigative searches. It achieves this by pre-calculating and storing results in a summary index, which makes querying significantly quicker when users look for specific insights across the data models.

When queries are executed, instead of needing to search the raw logs directly each time, the accelerated data model uses the pre-computed results to return information much more rapidly. This acceleration feature is particularly beneficial in scenarios where users frequently run searches or reports that require analysis of large datasets, as it reduces the time and computing resources needed for those queries.

In contrast, other functionalities such as restricting data ingestion rates, compressing historical logs, or automating incident response do not pertain directly to the performance improvement in search functionalities provided by Data Model Acceleration. These are separate aspects of data management and operation within Splunk that do not focus on the acceleration of data models for investigative purposes.