What are 'data models' in Splunk?

Data models in Splunk serve as hierarchical structures designed to accelerate search capabilities and provide a framework for better organizing, accessing, and analyzing data. They enable users to define a structured view of their data, creating a layer of abstraction that simplifies the search process. By utilizing data models, Splunk can optimize the performance of complex queries and provide a faster response when analyzing large datasets.

Data models are particularly valuable for integrating and categorizing data from various sources, allowing for more efficient data exploration through pre-defined features and relationships. They are especially effective in supporting Pivot, which is a user-friendly interface for non-technical users to manipulate and visualize data without requiring in-depth knowledge of search language.

Other choices, like examples of searchable queries or templates for user roles, do not accurately represent the function of data models. Similarly, while data storage techniques pertain to how data is saved and managed, they do not capture the essence of what data models accomplish within the Splunk ecosystem.