endorsedCompleted

Data Warehouse & ML Models for Natural Health Distributor

Services Covered:

AI and Machine Learning • Custom Software Development

Industry

E-commerce & Retail

Duration

21 months

Budget

50K - 199K

Start Date

1 February 2024

Team Size

1-5 Employees

Engagement Model

Offshore

Project Capability Score (PCS)

78/100

Good

Project Capability Score (PCS) estimates how capable inoxoft is of successfully delivering a project like this, based on its past experience, track record, and ability to handle similar work. Learn more about PCS

Client

Platinum Naturals

Richmond Hill, Canada

Project Summary

A natural health product distributor hired INOXOFT to build a Redshift-based data warehouse and develop machine learning models for device feedback. INOXOFT created ETL pipelines from MongoDB to S3/Redshift, improved query response times, optimized storage utilization, and increased data availability and accuracy. The team managed the project efficiently, maintained proactive communication with weekly check-ins, and was responsive throughout the engagement.

Key Challenges

Build a data warehouse using AWS Redshift
Develop machine learning models to provide feedback on Systemloco devices and data

Project Deliverables

Extract collections from MongoDB and store them in Amazon S3
Design and implement ETL pipelines to transform and load data into Amazon Redshift
Define schema and indexing strategies to optimize query performance
Ensure data consistency and integrity between MongoDB, S3, and Redshift
Provide documentation on data flow, transformations, and Redshift setup

Project Solution

INOXOFT built a Redshift-based data warehouse by extracting MongoDB collections to Amazon S3 and designing ETL pipelines to transform and load data into Amazon RedShift. They set up a centralized Redshift cluster, defined schema and indexing strategies to optimize query performance, ensured data consistency between MongoDB, S3, and RedShift, and provided documentation. They also developed machine learning models for battery performance estimation and zone-based movement predictions to provide feedback on Systemloco devices.

Project Outcome

ETL pipeline reliability
Reduction in query response time
Optimized storage utilization
Improved data availability and accuracy

Platforms

Cloud

Tech Stack

MongoDB
Amazon RedShift
Amazon Web Services (AWS)

Client Endorsement

Overall Review Rating

Timeliness

Cost Rating

Willing to Refer

Quality of Deliverables

“INOXOFT successfully built a reliable ETL pipeline that reduced query response time, optimized storage utilization, and improved data availability and accuracy. The team managed the project efficiently and was highly responsive, communicative, and friendly. They were also always willing to help.”
Darryl Piwek
Former Director of Operations
Date: 12/10/2025