A full-scale data modernization and migration, unifying fragmented enterprise data across SAP HANA, BigQuery, and multiple operational systems into a single Databricks-based data lake.
A leading American discount closeout retailer.
The client's data was fragmented across SAP HANA, BigQuery, and multiple enterprise tools, with no unified source of truth. This prevented real-time operational visibility, slowed analytics across regions, and created growing overhead as the business expanded its store footprint by 10% annually, making data modernization a priority for sustained growth.
QBurst consolidated scattered enterprise data into a unified Databricks data lake, using internal accelerators to reduce the manual engineering effort typically required for large-scale data unification.
Leading American discount closeout retailer offering deeply discounted, overstocked, and closeout products from name-brand and private-label suppliers. Experiencing rapid expansion and relying on a technology-driven retail model, the company focuses on operational efficiency, scalability, and personalized in-store experiences through data and digital transformation.
Data scattered across disconnected systems limited the client's ability to see and act on operations in real time.
We executed a full data migration and modernization initiative, consolidating the client's fragmented data landscape into a single Databricks-based data lake, using QBurst's internal accelerators to reduce the manual effort typically required to migrate and reconcile data at this scale.
-1790071880245.jpeg)
Client Profile
Challenges
Solution
Technical Highlights
Impact
