Identifiers represent real-world concepts within a business context, like customers, transactions, or ad campaigns. Analyses often revolve around specific identifiers such as customer churn or annual recurring revenue modeling. These identifiers are depicted in semantic models using id columns, acting as join keys linking other semantic models in the semantic graph.
Identifiers offer a clear structure to the underlying data, enabling more coherent and insightful analysis.
Every identifier requires a name and type. The name can reference the key column name from the data table, or function as an alias with the column name referenced in the expr parameter.
Name
Either refers to the key column name from the underlying data table or
serves as an alias with the column name referenced in the expr parameter.
Type
This identifies the identifier’s role in join logic and determines the
nature of the join. Types include: Primary, Foreign and Unique.
Expression (optional)
Indicates the underlying column that signifies the identifier. If not
provided, We default to the identifier name.
Identifiers are illustrated using these parameters:
identifiers:-name:[name]type:[Primary or Natural or Foreign or Unique]description:[description of the field or role]expr:[column name or expression, defaults to the name if not provided]
For instance, in a semantic model detailing sales, identifiers can be defined as:
Identifiers are foundational, establishing relationships and ensuring seamless data analysis. Through a keen understanding of identifiers and their applications, businesses can derive richer insights and make data-driven decisions.