Enterprise Data Platform Modernization

Problem

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 / Constraints

What shaped the work

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.

Approach

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.

Outcome

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.

My Role

Where I contributed most

Reporting-layer redesign, performance testing, and rollout coordination

Trade-offs / Lessons

Choices, constraints, and what mattered

• 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.