Our client is a U.S.-based logistics provider specializing in temperature-controlled transportation solutions for the food and beverage industry. Their end-to-end services are designed to maintain quality from point of origin to final delivery, supporting some of the most demanding environments in the logistics sector.
In just 1.5 months, we supported our client in fixing critical data discrepancies between their Datawarehouse and centralized data lake. Our team built custom reconciliation jobs and improved data integration workflows using Azure technologies, restoring trust in reporting and ensuring operational accuracy across all facilities.
The company has a standalone application for its customers, with a reporting module that uses data from a customer Datawarehouse (DWH). The customer’s WMS is one of the primary source systems that uses the data lake with a database wrapping. All new facilities are onboarded using this application stack, including deleting and recreating orders and receipts.
However, as the data lake was centralized any deleted customer entities would also be removed from the data lake, while corresponding entries remain in DWH, leading to incorrect reporting of inventory transactions and balance.
Our team improved integration processes and created custom reconciliation jobs, which compare data deltas.
The build enabled a system that marked entities/records and excluded from reporting when deleted from the source system. Although the reporting was cleansed all source data remains in a decentralized DWH for process analysis purposes.
Reconciliation logic ensures alignment between source systems and reporting layers, eliminating discrepancies caused by data deletions.
Reports now exclude invalid or deleted records, significantly improving the accuracy of inventory transactions and balance metrics.
Teams gained renewed confidence in reporting outputs, reducing second-guessing and manual validation efforts.
With a consistent integration approach, new facilities can now be added without introducing data misalignment.
Modular enhancements and cloud-based solutions support ongoing scalability and evolving business requirements.
