Case Studies & Solutions

Executive Summary Nomura’s Risk Advisory division transformed its data infrastructure by modernizing from an aging Hadoop environment to a hybrid object-native data lakehouse powered by MinIO AIStor. This shift delivered measurable gains in performance, stability, and scalability, reducing operational costs and risk, while improving data availability and supporting future AI and analytics workloads. Key benefits included: * Performance: Benchmarking demonstrated

1. Executive Summary A global financial institution modernized its Data Analytics Platform, shifting from legacy, appliance-based storage to a high-performance, cloud-native MinIO AIStor-powered data lakehouse. This transition cut deployment time by 50%, maximized data insights, boosted AI model efficiency, improved existing analytics workflows, and enabled entirely new AI-driven use cases. 2. The Environment Like many global financial institutions, this organization

Executive Summary AMD, a global leader in high-performance computing, graphics, and semiconductor technologies, faced challenges with its internal engineering systems. Their systems, including ServiceNow, Jira, GitHub repositories, telemetry pipelines, and infrastructure logs, were developed independently over time, but they were deeply interconnected. This created data silos that obscured the connections across the engineering lifecycle, hindering AMD's agility in

MinIO AIStor is now “Awardable” in the CDAO Tradewinds Marketplace. This confirms the solution as a secure, scalable, high-performance data foundation for AI.

Our customer, a large electric utility company with millions of users, was faced with challenges common to utility and energy companies around the world: how to manage ever-growing energy demands, while diversifying energy supply to incorporate renewable and sustainable sources. A key element of their strategy is a smart metering initiative - bringing the power of technology and AI to

Government agencies must embrace AI responsibly, ensuring data sovereignty and security are top priority. To obtain sovereign AI, the right foundation must be in place to ensure infrastructure, data, and models remain fully controlled across agencies.

The "big data king" that enterprises spent billions on is dying. Not from lack of trying, but because cloud-native alternatives now beat it at its own game. AIStor is faster and cheaper than HDFS. The revolution is happening in quarters, not decades.

Legacy systems drain budgets, slow innovation, and block AI progress. This article shows how phased modernization cuts costs, boosts performance, and builds a future-ready data foundation without disruption. Inaction is the real risk.

How does Exness handle massive data volumes and demanding AI/ML workloads? By moving to an on-prem infrastructure powered by MinIO. From scaling their data lake to managing traffic peaks of 200 Gbps, MinIO supports their AI workflows, disaster recovery, and more.