The bank’s primary limitation was its on-premises Oracle Exadata platform. Although highly capable, the environment could no longer keep pace with rapidly growing data volumes and the evolving demands of modern analytics, including AI/ML workloads, high-performance reporting, and on-demand scalability.
Key challenges included:
Limited scalability: Expanding the on-premises infrastructure required significant investment in hardware procurement, installation, and configuration, making scaling both costly and time-consuming.
Performance bottlenecks: Multi-terabyte database tables increasingly affected query performance, particularly for reporting workloads, while consuming resources needed by other critical business processes.
High Total Cost of Ownership (TCO): Maintaining physical infrastructure and Oracle licensing resulted in substantial operational costs
The client decided to migrate its entire enterprise data warehouse to the Snowflake Data Cloud, modernizing the entire analytics ecosystem and transitioning to a cloud-native architecture.
The transformation was designed as a multi-year, phased migration program. Pumanta supported the client throughout the entire journey—from the initial Proof of Concept (PoC) through full production deployment and enterprise-wide adoption. Beyond the technical implementation, we facilitated change management by delivering technical training, tailored knowledge-sharing initiatives, continuous user feedback sessions, responsive support, and adoption activities that helped embed the new platform across the organization.
Key solution pillars:
• Modern cloud architecture: The entire data warehouse architecture was migrated to Snowflake, including the staging layer, core data warehouse, and dedicated data marts.
• Custom migration framework: Due to the enormous data volumes, the complexity of the existing environment, and stringent banking security requirements, off-the-shelf tools were insufficient. Our team developed a suite of bespoke migration utilities in Python and SQL to automate schema conversion, data migration, incremental data loading, data synchronization, and automated data quality testing.
• Enterprise-grade security: The solution incorporated rigorous security controls, role-based access management, and GDPR-compliant governance mechanisms, fully aligned with financial regulatory requirements.
Key business outcomes:
• Significant performance improvement: Leveraging Snowflake’s elastic compute capabilities, analytical queries on massive datasets now execute several times faster, with some reporting workloads reduced from hours to just minutes.
• Virtually unlimited scalability: Computing resources can be scaled up instantly during peak periods—such as month-end financial closing—and scaled down when demand decreases, enabling continuous cost optimization.
• Improved user experience: Business analysts and data scientists gained near-instant access to data without waiting for shared infrastructure resources to become available.
• Simplified operations: Eliminating the need to manage physical infrastructure allowed the IT team to focus on delivering business value rather than maintaining database servers.
• Future-ready data platform: The new cloud architecture provides a scalable foundation for advanced analytics, machine learning, and AI-driven business initiatives.
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