How Lakehouse Modernization Improved Retail Analytics Performance at Scale

Overview

A retail enterprise needed a scalable lakehouse foundation to centralize analytics across sales, inventory, and customer domains. The goal was to improve data consistency and query performance while supporting high seasonal demand and expanding analytics use cases on Databricks.

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Snapshot:

• 35% improvement in query performance during peak demand periods
• 40% reduction in data processing latency across ingestion pipelines
• 50% faster onboarding of new data sources using standardized Medallion patterns
• 30% decrease in operational overhead through repeatable pipeline frameworks