Case Study

A leading automotive, houseware, and sports retailer illuminates a path forward for a long-stalled modernization to Azure
multi-category retailer

The client is a leading multi-category retailer with a strong presence in automotive, home, and outdoor goods, serving millions of customers nationwide. With an extensive network of stores and a well-established digital footprint, this retailer is a key player in the general merchandise and automotive services industry.

11
million

lines of code in C / C++ using Oracle database

4
weeks

to automatically map all explicit and implicit dependencies

“CAST Imaging provided critical dependency data to drive a tailored, low-risk modernization. This unlocked a journey the client had struggled to start for three years.”

Microsoft ISD Data & AI SSP

CAST Imaging enabled Microsoft Azure teams to automatically generate system blueprints, mapping internal dependencies and complexity in four weeks.

Challenge

Based in North America, the client – a leading multi-category retailer – has struggled for three years to modernize its homegrown, business-critical data warehouse management system.

This large monolithic system, consisting of 11 million lines of C/C++ and an Oracle database, required significant effort to keep stable and scale.

To meet evolving demands, the client aimed to modernize the application on Microsoft Azure.

Solution

In four weeks, Microsoft Azure teams used CAST Imaging to generate a system blueprint, mapping internal dependencies and complexity. Through architecture maps, code drill-downs, and impact analysis, they built a knowledge base of its inner workings.

The application was divided into modules with highlighted migration blockers, communication channels, and Oracle database connections, as well as recommended native services like Azure App Service, Azure Storage, Azure Web PubSub, and IaaS.

Results

With these insights, the client sped up its modernization strategy and reduced risks tied to legacy complexity. Comprehensive visibility into application architecture improved decision-making, while identified sustainability opportunities guided code quality enhancements for long-term stability. Clear data boundaries and refined modules simplified future scaling on Azure.

These gains also freed up funds to start modernization soon, aligning with evolving business needs and aims.