The Problem
“A legacy on-prem DB2 product database made metadata updates slow, access-heavy, and difficult to scale.”
Product updates were constrained by a legacy on-prem DB2 environment with limited access paths and slow operational workflows. Even routine metadata changes could take 1-2 days because teams had to work through older tooling, narrow access controls, and manual update processes. At Kroger's scale, that meant millions of product records and hundreds of fields could not be updated quickly enough for modern retail operations.
The Implementation
“Build an AI-assisted pipeline on Python, Databricks, and Azure to validate and execute large product updates.”
We developed a modernization pipeline using Python, Databricks, Azure, and OpenAI. The system translated requested product changes into validated batch operations, processed large product sets safely, and reduced the dependency on terminal-only DB2 workflows. Databricks provided newer data views and processing patterns so other Kroger teams could rely on cleaner, more accessible product data without repeatedly touching the legacy source system.
The Result
“Large product batches moved from day-scale manual updates to minute-scale modernization workflows.”
Batch updates that previously could take 1-2 days could now process roughly 100,000 products in 10-20 minutes. The work gave Kroger a faster path for product metadata changes, improved access to modern data views, and created a foundation other teams could use for downstream product data workflows.
