As organizations increasingly rely on data to drive strategic decisions, handling large volumes of data efficiently becomes a critical challenge. SAP Lumira, a leading self-service data visualization and analytics tool within the SAP ecosystem, enables users to explore and visualize complex datasets. However, managing large datasets in Lumira without sacrificing performance requires careful planning and best practices.
This article discusses effective strategies for managing large datasets and optimizing performance in SAP Lumira to ensure smooth, responsive analytics experiences.
- Slow Load Times: Large datasets can cause delays when loading or refreshing data.
- Performance Bottlenecks: Complex calculations and multiple visualizations on big data can degrade responsiveness.
- Memory Constraints: Lumira Desktop and Server operate within system memory limits, impacting the size of manageable data.
- Data Transfer Overhead: Importing large datasets from SAP HANA or other sources can create network and processing bottlenecks.
¶ 1. Data Preparation and Reduction
- Filter at Source: Use SQL queries or SAP HANA views to filter and aggregate data before importing into Lumira, reducing dataset size.
- Pre-aggregate Data: Summarize data at the appropriate granularity (e.g., monthly totals instead of daily transactions) to minimize volume.
- Remove Unnecessary Columns: Only import relevant columns to reduce data footprint.
- Use Live Data Connections: Connect directly to SAP HANA or BW systems using live connections to avoid data duplication.
- Push Computation to the Database: Utilize database processing power for calculations and aggregations instead of Lumira Desktop.
- Use Data Blending Sparingly: Combining multiple large datasets can slow performance; blend only when necessary.
- Limit Number of Visuals per Story: Too many charts on a single page increase load and rendering times.
- Use Efficient Chart Types: Prefer lightweight visualizations (bar, line) over complex ones (maps, heatmaps) for large data.
- Simplify Filters and Interactions: Avoid overly complex filters or interactions that require heavy processing.
- On Lumira Desktop, adjust JVM settings to allocate more memory for processing large datasets.
- Use Lumira Server caching to store query results, reducing repetitive database hits.
- For periodic data refreshes, use incremental loading methods instead of full reloads to improve efficiency.
- Utilize SAP tools such as SAP HANA Studio or SAP BW Monitoring to identify slow queries or bottlenecks.
- Analyze Lumira logs to detect performance issues.
- Create a HANA View to aggregate sales data by region and quarter.
- Connect Lumira Live to the HANA view for real-time access without data import.
- Limit Visualizations to key KPIs with summary charts.
- Use Filters to allow users to drill down without loading full datasets.
- Schedule Data Cache Refresh on Lumira Server to enhance user experience.
Effectively managing large datasets and optimizing performance in SAP Lumira requires a combination of smart data preparation, leveraging SAP’s backend systems, and efficient dashboard design. By following best practices such as using live connections, filtering data at the source, and tuning system resources, SAP professionals can deliver high-performing, scalable analytics solutions.
These strategies ensure that users gain timely insights from even the largest datasets without compromising on responsiveness or usability—ultimately enabling better data-driven decision-making across the enterprise.