In the fast-paced world of enterprise analytics, speed and responsiveness are critical. SAP Analytics Cloud (SAC) empowers organizations to analyze vast amounts of data, but the performance of these analyses heavily depends on how well the underlying data models are designed and optimized. Efficient data modeling is key to ensuring users get quick, seamless access to insights without delays or system bottlenecks.
This article explores best practices and strategies for optimizing data models for performance within SAP Analytics Cloud to maximize analytical efficiency and user satisfaction.
Data models in SAC are the foundational layer where data is prepared, structured, and connected before visualizations or stories are built on top. These models can be based on various data sources, such as live connections to SAP BW, SAP HANA, cloud databases, or imported datasets.
A well-designed data model ensures that queries run efficiently, data refreshes are fast, and the end-user experience is smooth.
Poorly optimized data models can lead to:
Optimizing your data models addresses these issues by improving query performance, reducing data volumes, and enhancing data processing speed.
SAC supports two primary types of data connections: Live Data Connection and Import Data Connection.
Live Data Connection: Data remains in the source system (e.g., SAP BW or SAP HANA), and SAC queries the data in real-time. This avoids data duplication and leverages the source system’s performance capabilities. However, optimization must focus on the source models and the queries executed.
Import Data Connection: Data is imported into SAC’s in-memory engine. This allows for faster query responses within SAC but requires careful management of data size and refresh schedules.
Selecting the appropriate connection depends on the use case, data volume, and performance requirements.
Reduce the size of the dataset by:
When using live connections to SAP HANA or BW, ensure that the source tables and views are properly indexed and partitioned to speed up query execution.
Utilize SAC’s built-in performance monitoring tools and logs to identify bottlenecks in queries and models. Analyze slow queries and optimize accordingly by refining filters, aggregations, or data structures.
Optimizing data models in SAP Analytics Cloud is a critical step toward delivering high-performance analytics solutions. By carefully selecting data connections, reducing data volumes, leveraging efficient model design, and monitoring performance, organizations can provide faster insights and a better user experience.
A robust and optimized data model not only accelerates decision-making but also enhances overall trust and adoption of SAP Analytics Cloud within the enterprise.