Case Study — HR Learning & Community Platform

100+ tables. One monolithic database. Rebuilt for the next phase — phased migration to MongoDB Atlas.

How an HR learning platform with 100+ database tables moved from a monolithic architecture to a modern, scalable document-store system on AWS.

Stack

100+

Tables assessed in schema analysis

MongoDB

Atlas Flex Tier architecture

30-50%

Infrastructure cost reduction

Phased

Incremental feature-by-feature migration

Before.

The platform had outgrown the database it launched on. 100+ tables in a monolithic structure that was never designed for evolving document schemas and community-driven features.

Every new feature added friction to the same structural bottleneck. Scaling was costing $900/month in server overhead, while the monolithic backend limited the platform's ability to support its growing 73,717 registered users and 12,575 monthly actives.

The Situation

Monolithic database handling relational and document data
Technical debt from 100+ tables with unused fields
High infrastructure costs ($900/mo baseline)
Rigid schema preventing rapid feature iteration

What we did.

Architected an incremental migration to a document-store foundation.

Step 1 — Schema Analysis & NoSQL Design

Assessed 100+ tables and designed a flexible MongoDB schema optimized for NoSQL access patterns, focusing on high-volume user activity.

Step 2 — Phased Migration Strategy

Adopted a two-phase approach: stabilize current UI on serverless APIs (Phase A), then migrate features to MongoDB incrementally (Phase B) to minimize risk.

Step 3 — MongoDB Atlas Foundation

Configured MongoDB Atlas Flex Tier with security, network access, and initial database collections ready for production data.

Step 4 — Data Transformation Framework

Developed transformation logic to migrate existing data from PostgreSQL to MongoDB format while maintaining referential integrity.

Step 5 — Team Training & Implementation

Provided 2 days of dedicated MongoDB training and pair programming to ensure the engineering team could maintain the new architecture.

After.

73,717

Users supported by a modern document architecture

< 300ms

Target API response time for 95% of requests

30-50%

Monthly infrastructure cost savings achieved

5-7 week

Total project timeline including full team training

< $150/mo

Final target infrastructure cost baseline

$7,500

Total project budget achieved (including implementation)

The Installed System.

Flexible Schema Foundation

A document-store architecture that supports rapid feature iteration, allowing the team to add new data fields without complex database migrations.

Incremental Cutover Gates

A phased migration framework that allows features to move to MongoDB one by one, ensuring zero platform downtime and clear rollback paths.

Serverless API Integration

MongoDB Atlas integrated with AWS Lambda and API Gateway, creating a low-cost, high-scale foundation that only costs for actual usage.

Jake McMahon

ProductQuant

10 years building growth systems for B2B SaaS companies at $1M–$50M ARR. BSc Behavioural Psychology, MSc Data Science. This engagement required performing a deep forensic audit of a 100+ table schema to architect an incremental migration strategy to MongoDB Atlas that resolved foundational technical debt while enabling rapid feature iteration.

What this looks like for your company

The Foundation.

A six-week engagement that includes data architecture design as part of a full growth infrastructure build — connecting your database structure to analytics, churn prediction, and product intelligence.

Data model assessment: which structures support or block growth analytics
Full analytics audit with 5–10 biggest gaps revenue-sized and implementation roadmap
Churn prediction model trained on your data; at-risk accounts surfaced weekly from week one
Competitive intelligence library with ongoing monitoring system
Full handover documentation; your team runs everything independently from day one

$15,000–$25,000 · 6 weeks

Right for you if

Data architecture decisions being made without a view of downstream analytics impact
Analytics queries running slow or unreliable due to schema design
Want data infrastructure that supports growth intelligence, not just transactional operations

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