How GeoPITS Reduced a Retail enterprise's Data Processing Time from 10 Hours to 45 Minutes with a Unified Azure Lakehouse
A modern lakehouse architecture using Azure Databricks, Delta Lake, and automated pipelines for analytics and machine learning.
Overview
The Challenge
The data was scattered between SQL Servers, APIs, and flat files without any kind of central structure.
Daily sales processing took over 10 hours, postponing insights into the business.
Fragmented tools lead to limited collaboration between data engineers and data scientists.
Unable to scale predictive analytics or support real-time decision-making.
Our Solution
Designed a Lakehouse architecture on Azure Databricks using Bronze, Silver, and Gold Delta tables.
Built ingestion pipelines using Azure Data Factory for SQL, API, and file-based sources.
Designed PySpark notebooks for cleaning, deduplication, and data enrichment.
Automated 40+ ETL and ML workflows using Databricks Workflows.
Governance implemented with Unity Catalog and a reusable feature store for the ML models.
Benefits
Business Impact
Faster Refresh of Data
Reduced daily data processing time from 10+ hours down to 45 minutes.
One Source of Truth
Unified data from all systems into a single, governed lakehouse for consistent reporting.
Advanced ML Adoption
Enabled the deployment of predictive models, such as sales forecasting, inventory planning, and customer retention.
Productivity Boost
Empowered more than 40 analysts and data scientists to work together in one ecosystem while reducing infrastructure costs by 35%.
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