Data Modernization in Insurance: From 14 Hours to Real-Time Decisions

Our insurance sector partner was struggling to manage rapidly growing data volumes with its existing infrastructure. ETL processes exceeded 14 hours, reports were delayed, and business units were forced to make decisions based on outdated data.

Their objective was clear: migrate the growing data workload to a fast, scalable, and future-ready architecture.

We designed and implemented a modern Data Lakehouse architecture running on Kubernetes and powered by Apache Iceberg. The traditional layered data structure was transformed into an open, flexible, and fully integrated platform built on open standards.

With the new architecture:

• 14-hour ETL processes were reduced to under 4 hours
• Iceberg’s time-travel and schema evolution capabilities significantly simplified data management
• Kubernetes infrastructure enabled dynamic and efficient resource scaling

This transformation went beyond technical performance improvements. It increased business units’ trust in data, accelerated decision-making processes, and strengthened operational agility.