The Challenge
The organization’s marketing analytics platform was originally built on Microsoft Azure, but it had reached its limits. Infrastructure costs were climbing, and the platform couldn't scale to meet the needs of expanding data volume and complexity. As analytics demand grew, particularly around campaign performance, patient engagement, and omnichannel outreach, the legacy setup created delays, manual workarounds, and missed insights. The team required a more flexible and cost-effective environment that could support rapid iteration and future AI/ML capabilities without compromising data quality or dashboard reliability.
The Solution
Productive Edge led a strategic migration of the analytics platform to AWS, rebuilding core data pipelines on Databricks and implementing a modern Lakehouse architecture. The team designed a modular, scalable environment that mirrored the existing analytical outputs while optimizing for speed and efficiency. Orchestration was handled using Airflow (via Astronomer), ensuring smooth job management across staging layers. The transition was carefully phased to minimize downtime and data disruption, and the new environment was designed with future AI workloads in mind. This included native support for Delta Lake, streamlined ETL, and room for advanced integration of ML models.
The Results
The migration delivered immediate and measurable results. Infrastructure costs dropped by approximately 40%, and analytics workflows were streamlined. The organization maintained full parity with its previous dashboards and reporting, ensuring continuity for business users while gaining a modern, flexible foundation. With Databricks on AWS, the system now supports larger data volumes, more frequent updates, and integration with third-party tools. Perhaps most importantly, the platform is now ready for advanced analytics use cases, including campaign optimization models and AI-powered patient engagement, without requiring additional rework.
