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The Ocient Hyperscale Data Warehouse

To deliver next-generation data analytics, Ocient completely reimagined data warehouse design to deliver real-time analysis of complex, hyperscale datasets.

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Ocient is uniquely designed for maximum performance and flexibility with always-on analytics, maximizing your hardware, cloud, or data warehouse as a service spend. You get predictable, lower costs (and absolutely zero headaches).

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Customer Solutions and Workload Services

Ocient offers the only solutions development approach that enables customers to try a production-ready solution tailored to their business requirements before investing capital and resources.

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Management Services

Tap into the deep experience of the Ocient Management Services team to set up, manage, and monitor your Ocient solution.

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About Ocient

In 2016 our team of industry veterans began building a hyperscale data warehouse to tackle large, complex workloads.

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Published July 2, 2024

Efficiently Scaling AI Data Infrastructure with Ocient

As the volume of data supporting AI applications grows ever larger, it’s critical to deliver scalable performance without overlooking power efficiency. This episode of Utilizing Tech, sponsored by Solidigm, brings Chris Gladwin, CEO and co-founder of Ocient, to talk about scalable and efficient data platforms for AI with Jeniece Wnorowski and Stephen Foskett. Ocient has developed a new data analytics stack focused on scalability with energy efficiency for ultra-large data analytics applications. At scale, applications need to incorporate trillions of data points, and it is not just desirable but necessary to enable this without losing sight of energy consumption. Ocient leverages flash storage to reduce power consumption and increase performance but also moves data processing closer to the storage to reduce power consumption further. This type of integrated storage and compute would not be possible without flash, and reflects the architecture of modern processors, which locate memory on-package with compute. Ocient is already popular in telco, e-commerce, and automotive, and the scale of data required by AI applications is similar, especially as concepts like retrieval-augmented generation are implemented. The conversation around datacenter, cloud, and AI energy usage is coming to the fore, and companies must address the environmental impact of everything we do.