Real-Time POS Analytics with Databricks Medallion Architecture

Problem Statement

The client is a leading global fashion footwear and accessories retailer operating across a wide brick-and-mortar footprint and a fast-growing e-commerce channel, with high-volume omnichannel POS transactions flowing through a mobile POS platform. Operating in a trend-driven, promotion-intensive category with compressed decision cycles across pricing, promotions, inventory, and store operations, the organization needed a retail POS analytics foundation that could match the pace of the retail floor. The goal was to replace batch-based reporting with a governed, near real-time single source of truth for retail performance, built on Azure Databricks and anchored in Medallion architecture.

Key Challenges

• Slow, batch-based reporting meant merchandising and store operations reacted to insights that had already aged out of relevance.
• Peak-hour traffic patterns, the exact moments the business needed to see, were the ones most delayed by batch cycles.
• High-volume, complex POS data with nested JSON payloads stressed downstream parsing, with no governed landing zone to standardize incoming data.
• Heavy reliance on a legacy ERP that could not stream, leaving business teams without a modern, queryable single source of truth.
• Limited pipeline stability and cost efficiency, with untuned DBU consumption, cross-join-heavy parsing, and manual failure recovery.

Solution Implemented

• Medallion Architecture: Built Bronze, Silver, and Gold pipelines on Azure Databricks using Auto Loader and Delta Live Tables (DLT).
• Governed Data Ingestion: Ingested POS data including Cash TLog, Product, Traffic, Store, Inventory, and Employee data into ADLS Gen2 as a single governed landing zone.
• Data Standardization: Standardized nested JSON payloads into cleaned, structured Silver tables ready for business consumption.
• Domain-Aligned Gold Layer: Built a Gold layer aligned to business domains so merchandising, operations, and finance could query the same trusted numbers.
• Performance Optimization: Re-engineered JSON parsing to reduce explodes and eliminate cross joins, lowering compute cost and improving runtime.
• Workflow & BI Integration: Orchestrated and scheduled pipelines through Databricks Workflows and connected Power BI directly to the Lakehouse for decision-ready dashboards.