Modernizing Retail Intelligence with Scalable Data Infrastructure
Unifying retail data pipelines to fuel real-time analytics and personalization
The Challenge
A major retail brand operating across 11 countries was relying on fragmented Excel reports and legacy ETL tools to track sales, inventory, and customer behavior. The system couldn’t handle the growing data volume, lacked real-time capabilities, and made decision-making painfully slow.
Our Approach
We built a cloud-native data lakehouse architecture using Azure Data Lake, Databricks, and Delta Lake for unified, scalable storage and analytics. Key engineering deliverables included:
- Near real-time ingestion pipelines from 1200+ retail locations via Kafka
- Automated batch-to-stream ETL migration with orchestration via Apache Airflow
- Dimensionally modeled data marts optimized for Power BI dashboards
- Data quality frameworks with Great Expectations and built-in alerting
Stats
Reporting: 12h → 15min
The Outcome
- Cut reporting latency from 12 hours to under 15 minutes
- Enabled dynamic pricing strategies with real-time inventory and footfall analysis
- Reduced data ops overhead by 60% through automated workflows
- Empowered business teams with self-serve analytics
Case Study
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