Performance Tuning
Enterprise Data Platform Modernization
A retail analytics platform needed a more maintainable reporting-layer design and better performance under concurrent usage as analytics adoption expanded.
Case study · Performance Tuning
Retail Dashboard Performance Tuning
Average dashboard load time dropped from 90 seconds to 7 seconds, restoring usability across five departments.
Evidence snapshot
- Outcome signal
- 90s to 7s average load time
- Load time dropped to 7 seconds through dataset and SQL logic changes
- Focus
- Dashboard performance tuning
- Industry: Retail
- My role
- Dataset redesign and SQL optimization
- Tools: Power BI, SQL, DAX
Summary
Average dashboard load time dropped from 90 seconds to 7 seconds, restoring usability across five departments.
What needed to change
A retail analytics dashboard used by five departments loaded in over 90 seconds, making it impractical for daily decision-making.
Context and constraints
Industry context: Retail. Primary focus: Performance Tuning. The scope and sequencing were shaped by concrete delivery constraints.
- 90-second dashboard load time across five departments
How the work was handled
Redesigned the dataset structure and optimized SQL query logic to remove the primary performance bottlenecks.
What changed in practice
Average dashboard load time dropped from 90 seconds to 7 seconds, restoring usability across five departments.
- Average dashboard load time dropped from 90 seconds to 7 seconds.
- The optimization applied across analytics used by five departments.
- The improvement came from dataset redesign and SQL logic changes.
Measurable performance proof tied to daily operational use — not a lab benchmark.
My role
Dataset redesign and SQL optimization
Tools: Power BI, SQL, DAX
Choices and lessons
- Redesigned the dataset structure to remove performance bottlenecks.
- Optimized SQL query logic to improve execution speed.
- Focused the work on analytics used daily across five departments.
Implementation detail
Highlights
- Reduced average dashboard load time from 90 seconds to 7 seconds.
- Optimization applied across analytics used daily by five departments.
- Improvement came from dataset redesign and SQL logic changes, not infrastructure scaling.
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