BW_ANALYTICSbeginner
BW Performance
Foundational and applied knowledge of BW performance engineering covering data load throughput, query response time, InfoProvider design impact, aggregation strategies, and diagnostic techniques across classic BW-on-HANA and BW/4HANA landscapes.
Overview
Foundational and applied knowledge of BW performance engineering covering data load throughput, query response time, InfoProvider design impact, aggregation strategies, and diagnostic techniques across classic BW-on-HANA and BW/4HANA landscapes.
Lessons in this topic
- Tuning DTP and Process Chain Load Performance for Large Data VolumesLearn how to configure Data Transfer Process (DTP) package sizing, parallel processing, and process chain design to reduce data load runtimes and avoid resource contention in SAP BW and BW/4HANA.
- Optimizing InfoProvider Design and Query Execution for Better PerformanceA practical look at how InfoProvider modeling choices—partitioning, compression, aggregates versus HANA-native aggregation, and CompositeProvider design—affect query and load performance, with concrete tuning techniques.
- Why BW Performance Matters: Where Time Is Spent in Loads and QueriesAn introduction to the core performance dimensions in SAP BW—data load time, transformation processing, and query response time—and why each matters to the business.
- Improving InfoProvider and Query Performance with Aggregation and Design ChoicesExplains practical techniques for improving InfoProvider and query performance including compression, partitioning, aggregates versus HANA-native optimizations, and query design choices.
- Query Runtime Optimization: Read Mode, Aggregates, and OLAP Cache StrategyLearn how BEx query read mode, aggregates/HANA views, and the OLAP cache interact to determine reporting speed, and how to choose the right combination for different InfoProvider and usage patterns.
- Why BW Performance Matters and Where Bottlenecks Come FromIntroduces the business impact of BW performance problems and the main areas where bottlenecks originate: extraction, transformation, storage model, and query execution.