Big Data Clusters: Building the Best Infrastructure Platform for Big Data Workloads

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Getting the benefits of big data analytics can be challenging, but it is a necessary endeavor for any organization to succeed going forward. Understanding the challenges to maximizing big data analytics and DL, and how to overcome them, is crucial. Determining your expectations up front, and carefully orchestrating your infrastructure build, will allow you to construct an architecture that is scalable and prepared for the demands big data applications will place on it. The Silicon Mechanics Triton Big Data Cluster can help make your big data analytics goals a reality. This reference architecture delivers a flexible, scalable platform that is robust enough to meet your high-speed data processing needs while ultimately reducing TCO.

Our friends over at Silicon Mechanics put together a guide for the Triton Big Data Cluster™ reference architecture that addresses many challenges and can be the big data analytics and DL training solution blueprint many organizations need to start their big data infrastructure journey.

The guide is for a technical person, especially those who might be a system admin in government, research, financial services, life sciences, oil and gas, or a similarly compute-intensive field. This individual may be tasked with making the organization’s big data strategic initiatives a reality by enabling efficient, scalable access to data and finding ways to extend the value of their IT investment while still meeting computing needs.

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