![]() Each dataset within a data model can be used to generate a search that returns a particular dataset. When you consider what data models are and how they work it can also be helpful to think of them as a collection of structured information that generates different kinds of searches. A data model's permissions cover all of its data model datasets. See Dataset field types.ĭata models are a category of knowledge object and are fully permissionable. The fields that data models use are divided into the categories described above (auto-extracted, eval expression, regular expression) and more (lookup, geo IP). Data model datasets can get additional fields at search time through regular-expression-based field extractions, lookups, and eval expressions. Each child dataset in a dataset hierarchy can have new fields in addition to the fields they inherit from ancestor datasets.ĭata model datasets can get their fields from custom field extractions that you have defined. Each of these root datasets can be the first dataset in a hierarchy of datasets with nested parent and child relationships. Meanwhile, a data model derived from a heterogeneous system log might have several root datasets (events, searches, and transactions). But these child dataset do not contain additional fields beyond the set of fields that the child datasets inherit from the root dataset. The root dataset may have child dataset beneath it. csv file, the resulting data model is flat, with a single top-level root dataset that encapsulates the fields represented by the columns of the table. This information can affect your data model architecture-the manner in which the datasets that make up the data model are organized.įor example, if your dataset is based on the contents of a table-based data format, such as a. To create an effective data model, you must understand your data sources and your data semantics. ![]() When you plug them into the Pivot Editor, they let you generate statistical tables, charts, and visualizations based on column and row configurations that you select. If you are familiar with relational database design, think of data models as analogs to database schemas. Each child dataset represents a subset of the dataset covered by its parent dataset. Data models are composed of datasets, which can be arranged in hierarchical structures of parent and child datasets. Then they select a dataset within that data model that represents the specific dataset on which they want to report. When a Pivot user designs a pivot report, they select the data model that represents the category of event data that they want to work with, such as Web Intelligence or Email Logs. These specialized searches are used by Splunk software to generate reports for Pivot users. It encodes the domain knowledge necessary to build a variety of specialized searches of those datasets. In building a typical data model, knowledge managers use knowledge object types such as lookups, transactions, search-time field extractions, and calculated fields.Ī data model is a hierarchically structured search-time mapping of semantic knowledge about one or more datasets. These knowledge managers understand the format and semantics of their indexed data and are familiar with the Splunk search language. ![]() Splunk knowledge managers design and maintain data models. Data models can have other uses, especially for Splunk app developers. Data models enable users of Pivot to create compelling reports and dashboards without designing the searches that generate them.
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