The Background:

A leading global digital music company operating across multiple international markets required automation and standardization of its financial data processes. With revenue transactions recorded across geographies and currencies, and financial data stored in distributed file-based systems, the organization needed a structured, scalable automation framework to improve reporting accuracy, operational efficiency, and banking compliance.

Business Challenge:

• ⚫MongoDB used for application data but not integrated with analytics ecosystem
• ⚫PostgreSQL RDS acting as a reporting layer causing performance bottlenecks
• ⚫Analytical queries impacting transactional workloads in PostgreSQL
• ⚫Heavy dependency on indexing strategies to support reporting queries
• ⚫Continuous index tuning required to maintain performance
• ⚫Index bloat increasing storage overhead and reducing efficiency
• ⚫Complex joins and aggregations still resulting in slow query performance
• ⚫Batch-based data flow from MongoDB → PostgreSQL → Power BI
• ⚫Lack of real-time visibility into project execution and site operations
• ⚫Fragmented data across project, material, equipment, and workforce systems
• ⚫Limited scalability and concurrency for enterprise reporting
• ⚫High dependency on manual data aggregation and reconciliation