What are accelerated data models utilized for in Splunk ES?

Accelerated data models in Splunk Enterprise Security are designed to enhance search performance by precomputing queries and storing the results in a more efficient manner. This approach allows for faster access to data, significantly speeding up searches that utilize these models. When a data model is accelerated, Splunk performs the heavy lifting of aggregating and summarizing the data at the time of data ingestion, instead of during the search process. This precomputation can dramatically reduce the time it takes to retrieve and analyze data, particularly for complex searches that involve large datasets.

The use of accelerated data models is particularly beneficial in scenarios where frequent searches are run on the same datasets, allowing users to leverage the pre-calculated results without having to waste time reprocessing the underlying data. This improvement in performance is essential in security contexts, where timely data analysis is critical for identifying and responding to threats effectively.

Other options relate to different functionalities within Splunk. Enhancements in storage capacity or user permission management do not directly pertain to how accelerated data models operate or improve search times. Similarly, while integrating external data sources is important, it does not have a direct impact on the performance improvements offered by accelerated data models.