Data aggregation is a fundamental technique in data warehousing, enabling faster query performance and providing summarized insights to support strategic decision-making. Within the SAP ecosystem, SAP Data Warehouse Cloud (SAP DWC) offers a robust, cloud-native platform for building scalable, flexible, and performant aggregated data models.
This article explores best practices, concepts, and technical approaches for building aggregated data models in SAP DWC, helping organizations optimize their data warehouse design and analytics capabilities.
Aggregated data models are data representations that consolidate detailed transactional data into summarized forms based on specified dimensions or key metrics. For example, sales data aggregated by month and region rather than at individual transaction level.
Benefits of aggregation include:
- Improved query performance by reducing data volume
- Simplified reporting for business users
- Faster analytics on high-level trends and KPIs
SAP DWC enables creation of aggregated models to:
- Optimize performance for analytical workloads.
- Provide semantic layers that are business-friendly.
- Support multi-level data granularity within a single data warehouse.
- Enable self-service reporting by empowering business users with simplified datasets.
SAP DWC uses Spaces as logical containers for datasets, models, and views. Typically, aggregation models are created within dedicated Spaces aligned to business functions or reporting domains.
- Graphical Views: User-friendly, drag-and-drop interface to build aggregation logic.
- SQL Views: Advanced option for custom aggregation queries using SQL.
Aggregation models often build upon raw or staged datasets imported or replicated from source systems via SAP SDI, SLT, or Data Intelligence.
- Identify key metrics and dimensions for aggregation (e.g., sales amount by region and month).
- Determine aggregation levels and granularity based on user needs.
- Use raw or cleansed data models as the base.
- Ensure source data is consistent and appropriately partitioned.
- In SAP DWC’s modeling interface, create a new Graphical View.
- Add source tables or views containing detailed data.
- Use aggregation functions (SUM, COUNT, AVG, MIN, MAX) on key measures.
- Group data by relevant dimensions (time, geography, product, etc.).
¶ Step 4: Define Filters and Parameters
- Implement filters to restrict data (e.g., current fiscal year).
- Use parameters for dynamic aggregation or user-driven filters.
¶ Step 5: Validate and Test
- Test aggregation logic with sample queries.
- Verify correctness of totals, averages, and grouping.
- Expose the aggregated views for consumption by business users.
- Register them in Spaces aligned to analytical domains.
- Use aggregated models as data sources in SAP Analytics Cloud or other BI tools.
- Design with the End User in Mind: Create aggregation levels that match reporting needs.
- Use Consistent Time Dimensions: Employ standard calendar or fiscal period dimensions.
- Minimize Data Redundancy: Reuse existing models and avoid duplicating data unnecessarily.
- Optimize Performance: Leverage partitioning, pushdown processing, and proper indexing.
- Layer Your Models: Build from detailed base models to aggregated models, and then to consumption models.
- Document Models Clearly: Maintain metadata and annotations for clarity.
Suppose a company wants to analyze sales performance aggregated by region and month:
- Base Model: Transaction-level sales data with columns like sales_date, product_id, region_id, sales_amount.
- Aggregated Model: Create a graphical view in SAP DWC that sums sales_amount, grouped by month and region.
- Consumption: Use this aggregated model in SAC dashboards to show trends without the overhead of querying transactional data.
¶ Challenges and Considerations
- Latency vs. Freshness: Aggregated data may introduce latency; consider real-time vs. batch update strategies.
- Complexity of Aggregations: Complex calculations or many aggregation layers may impact maintenance.
- Data Governance: Ensure proper access controls to aggregated datasets.
- Balancing Detail and Summary: Avoid losing critical detail when aggregating.
Building aggregated data models in SAP Data Warehouse Cloud is a vital strategy to enhance performance and simplify analytics across the enterprise. By leveraging SAP DWC’s powerful modeling capabilities, organizations can create scalable, reusable, and business-aligned aggregated datasets that accelerate insight generation and support strategic decision-making.