As organizations increasingly rely on data-driven decision-making, SAP Lumira serves as a powerful self-service analytics tool enabling business users to visualize and analyze vast amounts of data. However, when dealing with large datasets, performance challenges such as slow loading times, laggy interactions, and delayed visual rendering can arise, impacting user experience and productivity.
This article outlines best practices and strategies to optimize performance when working with large datasets in SAP Lumira, ensuring smooth, responsive, and effective data analysis.
Handling large volumes of data can strain SAP Lumira due to:
- High memory consumption during data loading and processing.
- Complex calculations or aggregations on massive datasets.
- Network latency when accessing remote data sources.
- Inefficient data models or unoptimized visualizations.
Addressing these issues early in the design and development process is crucial.
- Leverage SAP BW, SAP HANA, or other backend systems to perform aggregation and filtering before data reaches Lumira.
- Use aggregated views or cubes to reduce data volume.
- Push complex calculations to the database to minimize client-side processing.
¶ 2. Use Data Source Filters and Query Limits
- Apply filters in the data acquisition step to limit rows and columns loaded into Lumira.
- Use query restrictions on the dataset, such as date ranges or specific categories.
- Avoid loading unnecessary detailed data when summary data suffices.
¶ 3. Optimize Data Model and Structure
- Remove unused columns and dimensions.
- Convert string dimensions with many unique values to hierarchies or grouped categories.
- Avoid calculated columns on large datasets; instead, create calculated measures or perform calculations in the source system.
- Use simple charts for large datasets (e.g., bar charts, line charts) instead of complex visuals like maps or scatter plots which require more rendering power.
- Limit the number of visualizations per story page.
- Avoid excessive use of animation or highly interactive components that may slow down rendering.
¶ 5. Utilize Data Blending and Incremental Loading
- Blend data from multiple smaller datasets instead of loading one massive dataset.
- Use incremental data loading or paging when supported to fetch data in chunks.
¶ 6. Leverage SAP Lumira Server and In-Memory Capabilities
- Deploy Lumira Server with sufficient memory and CPU resources.
- Use SAP HANA as a backend to exploit its powerful in-memory analytics.
- Enable caching features to speed up repeated queries.
- Use performance monitoring tools to identify bottlenecks.
- Analyze query execution plans in SAP HANA or BW.
- Refine dataset design and filters based on usage patterns.
A retail chain using SAP Lumira to analyze point-of-sale data across thousands of stores experienced slow dashboard load times due to millions of transaction records. By pushing aggregations to SAP BW queries, filtering data to recent quarters, and simplifying dashboard visuals, they reduced load times by over 70%, significantly improving analyst productivity.
Optimizing performance for large datasets in SAP Lumira requires a combination of strategic data preparation, smart visualization choices, and leveraging backend system strengths. By following these best practices, SAP professionals can ensure a seamless, responsive analytics experience even when working with vast and complex data, empowering users to derive insights quickly and confidently.