United States
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Executive Summary
To support rapid product expansion and resolve escalating cloud operational costs, a premier global consumer credit company engineered a massive infrastructure migration to a modern, cloud-native architecture. By leveraging Cloudera on AWS, the enterprise successfully transformed its flagship analytics sandbox platform. The strategic collaboration with Cloudera and Amazon Web Services (AWS) enabled the organization to conquer multi-tenant scalability challenges, dramatically lower cloud infrastructure expenses, and accelerate data-driven innovation for the financial services industry.
About the Customer
The customer is a global corporate leader in consumer credit information services, operating across 30 countries with over 21,000 employees. The enterprise reports an impressive $6.5 billion in annual corporate revenue, with its North American market delivering $4.4 billion of that total.
A premier offering in the company's portfolio is its advanced analytics sandbox product, which grants financial institutions direct access to the consumer credit universe. This platform empowers banks and credit unions to combine their internal data with exclusive market insights to confidently predict loan defaults, build accurate forecasts, and minimize lending risks. Currently, this platform manages and extracts actionable insights from vast datasets covering 245 million consumers and commercial entities. The platform actively serves between 30 and 40 of the world's top financial service companies, establishing itself as a dominant ecosystem for testing financial models and maximizing marketing budgets.
The Business Challenge
Despite the analytics sandbox generating extraordinary revenue, exceeding $500 million annually, the enterprise encountered severe technical and economic hurdles. Operating on an aging, legacy architecture and Amazon EMR, the platform’s cloud consumption costs skyrocketed to roughly $300,000 per month. These exorbitant operating expenses (OpEx) squeezed profit margins and jeopardized the commercial viability of the sandbox product.
Furthermore, as the daily transaction volumes scaled into the tens of billions, the technical infrastructure strained under the weight of managing over 200 global data warehouses. The multi-tenant environment suffered from "noisy neighbor" issues, where resource contention led to performance degradation and limited agility. The enterprise needed a cohesive platform capable of isolating computing resources, natively supporting a profusion of self-service analytics tools (such as SAS, Tableau, H2O, and Amazon SageMaker), and adhering to strict privacy regulations governing sensitive financial data. The company urgently required a cost-effective, auto-scaling architecture that could unify data processing while completely eliminating operational data silos.
The Technical Solution
To overcome these structural limitations, the organization strategically migrated its multi-tenant sandbox ecosystem to Cloudera on AWS. Working closely with Cloudera Professional Services and Engineering, the company phased out its legacy systems to embrace a highly resilient, multi-tenant cloud infrastructure.
A cornerstone of this technical triumph was custom-engineered automation. The enterprise tasked Cloudera to drastically improve its auto-scaling capabilities; the resulting Cloudera on AWS environment was engineered to dynamically spin up and allocate massive computing resources to handle demand spikes in under two minutes. Once analytics workloads conclude, the environment automatically scales back down to zero, eliminating idle infrastructure waste.
To satisfy stringent security and compliance requirements, the deployment integrated Cloudera Shared Data Experience (SDX) to establish unwavering enterprise data governance, precise data lineage, and role-based access control. By implementing robust data masking and synthetic data generation capabilities, the company paved the way for advanced machine learning and AI model training without risking sensitive data exposure.
Business Outcomes and Financial KPIs
The migration from legacy infrastructure to Cloudera on AWS resulted in profound, measurable business impacts that successfully resolved the organization's core challenges:
Massive Cost Reductions: By decommissioning the legacy cloud environment in fewer than 30 days, the enterprise realized an immediate and consistent reduction in monthly operational costs of $100,000 to $200,000. The internal cost per customer fell drastically compared to the legacy Amazon EMR solution.
Accelerated Revenue Growth: Removing infrastructural bottlenecks and reducing required personnel training allowed the company to rapidly enter new regional and international markets, driving product revenue growth of over 30%. The sandbox platform continues to securely generate $500 million in annual revenues.
Operational Efficiency & Speed-to-Market: The cloud-native elasticity enables experiential automation. Prospective financial customers can now register for trials via a web portal, immediately triggering a fully automated workflow that seamlessly provisions a new analytics environment. This breakthrough slashes time-to-value, empowering customers to validate features immediately and drastically improving sales conversion rates.
Security & Scale: The transition eliminated complex maintenance tasks and noisy-neighbor latency, securely processing queries against the 245 million consumer footprint and supporting tens of billions of transactions while maintaining strict regulatory compliance.
Continuous Improvement and Future State
The organization's ongoing roadmap includes leveraging Cloudera Data Services to expand its analytics reach into mid-market customer segments. By enforcing strict resource isolation within a multi-tenant cloud framework, the company can deliver the robust security profiles of a single-tenant environment at drastically lowered price points. Moving forward, the enterprise views Cloudera and AWS not merely as vendors, but as collaborative innovation partners, proactively shaping a unified ecosystem for data ingestion, analytics, and responsible enterprise Generative AI deployments.
Story developed in April 2026
