Implementation of a robust Big Data Analytics platform for a Fortune 500 company
Challenge
A Fortune 500 US retailer had a fragmented reporting process because data was siloed across multiple legacy systems. This caused inconsistent reports, weak visibility into key metrics, poor data quality (hurting inventory tracking/forecasting), and slow decision-making. They needed a unified analytics foundation for accurate, scalable insights.
A Fortune 500 US retailer had a fragmented reporting process because data was siloed across multiple legacy systems. This caused inconsistent reports, weak visibility into key metrics, poor data quality (hurting inventory tracking/forecasting), and slow decision-making. They needed a unified analytics foundation for accurate, scalable insights.
Solution
ZoolaTech built a unified, scalable Big Data analytics platform with real-time streaming and event-driven processing. Kafka streams business events; Java microservices process them; Airflow orchestrates cleaning and transformation; data is centralized in Teradata (with a planned move to BigQuery). Tableau dashboards deliver self-serve reporting, while ML models power forecasting and optimization.
ZoolaTech built a unified, scalable Big Data analytics platform with real-time streaming and event-driven processing. Kafka streams business events; Java microservices process them; Airflow orchestrates cleaning and transformation; data is centralized in Teradata (with a planned move to BigQuery). Tableau dashboards deliver self-serve reporting, while ML models power forecasting and optimization.
Results
The new platform enabled real-time visibility into previously siloed data and delivered actionable Tableau reporting for faster decisions. ML improved logistics (route optimization), pricing (forecasting), and delivery-time prediction. Inventory tracking accuracy increased from ~60% to 90%+, improving operations and customer experience while reducing costs through better forecasting and planning.
The new platform enabled real-time visibility into previously siloed data and delivered actionable Tableau reporting for faster decisions. ML improved logistics (route optimization), pricing (forecasting), and delivery-time prediction. Inventory tracking accuracy increased from ~60% to 90%+, improving operations and customer experience while reducing costs through better forecasting and planning.