Case Study

Centralized, managed data lake helped global retailer accelerate analytical insights

With many different relational database management systems across its enterprise, the company wanted to implement a managed data lake to consolidate information and streamline processing to gain insights for better sales and service decisions.


This American multinational retailer operates more than 10,000 stores in various formats and has a robust footprint of e-commerce websites around the world. Focusing on value and economic opportunities, the company employs millions of associates.


Streamlining data ingestion and processing

The retailer wanted to deploy a managed data lake with an extract, load, transform (ELT) framework to simplify data ingestion processes and also:


Delivering a centralized data lake for data-driven decision-making

After assessing the retailer’s data analytics needs, UST created a big data architecture design that consolidated data from disparate sources in a Hadoop storage system for easy processing and curation. During the engagement, UST:


Harnessing data for informed decision-making

The new data lake accelerated analytical insights, eliminated data errors and increased developer productivity. Meanwhile, business users gained a holistic view of customers and operational processes to improve staffing, product selection, service and support.