Case Study Disclaimer
With over 1.1+ million global users relying on Amazon Relational Database Services (AWS RDS) for mission-critical workloads, even small inefficiencies create significant downstream impact, which are reflected in the form creation setup and the database onboarding journey.
Over the course of this 7-month graduate capstone, I led research and design efforts within a team of 4 to rethink how users understand and manage their databases.
Skills
Mixed-method research
Product design
AI prototyping
Usability testing
Project management
My Role
Researcher
Designer
Workshop facilitator
Project manager / organizer
Timeline
7 months, Q2-4 2025
Industry
Cloud Database Infrastructure
Enterprise UX
What is AWS RDS?
As a new designer on this project, these terms flew right over my head
Amazon Relational Databse Services (AWS RDS) is a cloud tool that helps people set up and manage databases at scale.
Much like a coffee machine for databases: you pick your type, press a few buttons, and it brews everything for you without needing to understand the inner mechanics.
But that beginner’s confusion became my greatest asset.
It helped me design for others who might feel just as overwhelmed stepping into cloud computing for the first time.
Problem
Constraints in problem-solving
Domain complexity
This meant that I had to deep-learn the uncharted waters: interviewing database practitioners to understand their workflows, unpack technical jargons, and identify the most critical JTBD's before ideation began.
AWS system thinking
AWS’s ecosystem is also highly interconnected, so designing for one service or persona required us to consider ripple effects across many others.
Research Process
A New Streamlined, Intuitive RDS Workflow
A simplified journey that boosts user confidence and completion rate
Simplified database setup ->
Higher adoption rate
Guided AWS Console ->
Improved JTBD time +
Higher retention rate
