Performance Tuning
Retail Dashboard Performance Tuning
A retail analytics dashboard used by five departments loaded in over 90 seconds, making it impractical for daily decision-making.
Case study · Performance Tuning
Enterprise Data Platform Modernization
One analytics path went from about 25 million rows to under 100,000 through the pre-aggregation redesign. In later test rounds under 30-user concurrency, identified issues dropped from 43 to 2 after a combined model and infrastructure tuning pass.
Evidence snapshot
- One reporting path
- 25M to <100K rows
- Reduced through pre-aggregation and use-case-specific reporting structures.
- Concurrency test
- 30 users
- TabJolt-based load testing for interactive dashboard usage.
- Issue count
- 43 to 2
- Later test round after architecture and infrastructure tuning.
Summary
One analytics path went from about 25 million rows to under 100,000 through the pre-aggregation redesign. In later test rounds under 30-user concurrency, identified issues dropped from 43 to 2 after a combined model and infrastructure tuning pass.
What needed to change
A retail analytics platform needed a more maintainable reporting-layer design and better performance under concurrent usage as analytics adoption expanded.
Context and constraints
The modernization effort had to improve performance without breaking downstream reporting for a broader analytics rollout.
- Monolithic reporting structures were too heavy for broader self-service usage.
- Performance work had to combine model redesign, load testing, and infrastructure changes rather than one isolated fix.
How the work was handled
Redesigned the consumption layer into subject-specific models and pre-aggregated tables, ran TabJolt-based concurrency testing across the analytics stack, and coordinated with the platform team on the infrastructure tuning needed alongside the model changes.
What changed in practice
One analytics path went from about 25 million rows to under 100,000 through the pre-aggregation redesign. In later test rounds under 30-user concurrency, identified issues dropped from 43 to 2 after a combined model and infrastructure tuning pass.
- Split monolithic reporting structures into subject-specific models and pre-aggregated tables.
- Reduced one analytics path from about 25 million rows to under 100,000 rows.
- Ran 30-user load tests and drove later rounds where identified issues dropped from 43 to 2.
Shows architecture-level performance work with rollout-readiness evidence, not dashboard polish.
My role
Reporting-layer redesign, performance testing, and rollout coordination
Tools: AWS Redshift, Tableau, TabJolt, Databricks
Choices and lessons
- Used Redshift and Tableau with targeted pre-aggregation to remove known bottlenecks.
- Combined TabJolt-based concurrency testing with infrastructure tuning, not dashboard-only tweaks.
- Paired model changes with rollout communication so users could adapt during the cutover.
Implementation detail
Highlights
- Consumption-layer redesign turned one analytics path from roughly 25M rows to under 100K.
- 30-user concurrency testing surfaced issues that dashboard-only tuning would have missed.
- Paired model redesign with rollout communication so users could adapt to the new structure.
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