Subject Area: SAP-Data-Warehouse-Cloud
Modern data warehouses must support complex and scalable data models that serve both operational and analytical needs. SAP Data Warehouse Cloud (SAP DWC), with its cloud-native architecture, provides a robust platform for advanced data modeling using both relational and multi-dimensional paradigms. Understanding when and how to apply these models can significantly impact performance, usability, and business insight.
This article explores how to leverage both multi-dimensional and relational models effectively in SAP DWC for advanced data modeling scenarios.
Relational modeling organizes data into normalized tables with clearly defined relationships. It's ideal for transactional or operational data with frequent updates and a need for data integrity.
Key features:
Multi-dimensional modeling (also known as star schema or snowflake schema) is optimized for analytical queries and reporting. It organizes data into facts and dimensions, which enables fast aggregation and slicing/dicing of data.
Key features:
SAP DWC supports both approaches, providing flexibility to model data based on business needs:
When working with complex data from various sources, relational models provide a structured foundation.
Modeling sales transactions, with separate tables for:
You can:
Best Practices:
Once relational data is modeled and cleaned, use the Business Builder to create semantic models that serve as input for analytics.
An analytical dataset for Revenue by Region and Time, combining:
This model can be directly consumed in SAP Analytics Cloud (SAC) for dashboarding and reporting.
Best Practices:
SAP DWC allows for a hybrid modeling approach, which is especially powerful in large enterprise landscapes.
This layered approach ensures data governance, performance, and reusability across teams.
Advanced data modeling in SAP Data Warehouse Cloud leverages the strengths of both relational and multi-dimensional paradigms. Relational models provide a robust backend for storing and transforming raw data, while multi-dimensional models offer powerful analytical capabilities for end-users.
By understanding when and how to use each modeling technique, and combining them in a structured workflow, organizations can maximize the value of their data, enable business agility, and ensure trustworthy insights across departments.
Tags: SAP DWC, Data Modeling, Star Schema, Relational Model, Analytical Dataset, Semantic Layer, SAP Analytics Cloud, Business Builder, Data Builder