The company operates more than 600 stores across India and manages sales, loyalty, and supply chain data across multiple systems. As the data footprint grew, the business needed a unified analytical foundation that could support faster reporting, advanced machine learning models, and closer collaboration between engineering and analytics teams.

Business Challenges

As transaction volumes and store count grew, the existing data environment struggled to keep pace with reporting and analytics demands.

Fragmented data sources

Sales, loyalty, and supply chain data was scattered across SQL Servers, APIs, and flat files with no central structure.

Slow daily processing

Daily sales processing took over 10 hours, delaying insight into the business.

Limited collaboration

Fragmented tools led to limited collaboration between data engineers and data scientists.

Constrained analytics

The environment could not scale predictive analytics or support real-time decision-making.

Project Objectives

Geopits partnered with the retail enterprise to build a unified, governed lakehouse capable of supporting faster reporting and advanced analytics at scale.

Key Goals:

Consolidate scattered data into a single governed platform
Cut daily processing time from hours to minutes
Enable collaborative, scalable machine learning workflows

Solution Provided by Geopits

Geopits designed and implemented a modern lakehouse architecture focused on governance, automation, and cross-team collaboration.

Lakehouse architecture design

Bronze, Silver, and Gold Delta Lake layers built on Azure Databricks.

Automated ingestion pipelines

Azure Data Factory pipelines built across SQL, API, and file-based sources.

Data engineering and enrichment

PySpark notebooks for cleaning and deduplication, with 40+ ETL and ML workflows automated through Databricks Workflows.

Governance and feature store

Unity Catalog governance paired with a reusable feature store for machine learning models.

Results & Business Impact

The modernization initiative improved processing speed, governance, and collaboration across the business.

Capabilities

Before

After

Processing time

Over 10 hours daily

45 minutes daily

Data governance

Fragmented, no central structure

Unified and governed with Unity Catalog

Collaboration

Siloed engineering and analytics teams

40+ analysts and scientists in one ecosystem

Infrastructure cost

Higher, redundant systems

35% lower

Key outcomes

13× Faster Data Processing

Migrating to a governed Delta Lake architecture reduced daily data processing time from over 10 hours to just 45 minutes, enabling same-day access to sales insights.

35% Infrastructure Cost Reduction

Consolidating multiple, scattered systems into a unified lakehouse reduced redundant infrastructure and operational costs by 35%.

Advanced ML Adoption

A unified feature store and governed data foundation enabled the deployment of predictive machine learning models for sales forecasting, inventory planning, and customer retention.

Conclusion

Geopits partnered with the retail enterprise to modernize its data infrastructure and build a unified, governed foundation for faster reporting and advanced analytics at scale.

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At Geopits, our mission is to empower businesses to achieve reliable, secure and high-performing database environments and to derive meaningful business insights.