In enterprise reporting and analytics, time-dependent data is critical. Many business scenarios require analyzing data as it was valid at a certain point in time or over a specific period — for example, pricing changes, organizational hierarchies, employee statuses, or material master data variations.
When working with SAP HANA Live and Core Data Services (CDS) views, handling such time-dependent data correctly is essential for delivering accurate and meaningful insights. This article explores how time-dependent data is managed in CDS views, its importance, and practical approaches for implementation in SAP analytics landscapes.
Time-dependent data changes over time, and its correctness depends on applying the right validity period or point-in-time context when reporting.
Examples include:
CDS views provide several mechanisms to handle time-dependent data effectively:
Most SAP tables related to master data or conditions include valid-from and valid-to date fields (e.g., BEGDA and ENDDA). CDS views can filter data based on these fields by applying predicates to restrict results to the relevant time period.
Example:
@Consumption.filterable
define view Z_Customer_Master as select from KNA1
association [0..*] to Z_Customer_Validity as _Validity on $projection.Customer = _Validity.Customer
{
key KNA1.KUNNR as Customer,
KNA1.NAME1 as CustomerName,
_Validity.BEGDA,
_Validity.ENDDA
}
where _Validity.BEGDA <= :p_date and _Validity.ENDDA >= :p_date
Here, :p_date is a parameter representing the point-in-time for filtering valid records.
CDS views support input parameters to allow consumers to specify a date or date range dynamically at runtime. This enables flexible querying for different time slices.
Associations in CDS can be made time-dependent by including validity criteria in the join conditions, ensuring that linked data is relevant to the selected period.
Annotations such as @Semantics.validity.start and @Semantics.validity.end help tools and frameworks understand which fields represent validity periods, enabling better integration and UI support.
SAP HANA Live’s calculation views provide operational real-time data but typically require explicit handling of time-dependent logic. When modeling CDS views on top of or alongside HANA Live views:
Handling time-dependent data correctly in CDS views is fundamental to delivering accurate, historically relevant analytics within SAP HANA Live and broader SAP landscapes. Through parameterization, validity filtering, and semantic annotations, CDS views provide a robust framework to model temporal aspects of data.
This enables businesses to analyze trends over time, maintain audit trails, and ensure reports reflect the true state of data as it was valid—ultimately empowering better, time-aware decision-making.