Reducing Infrastructure Spend on Heavy Analytics Workloads

Summary

Engineering teams are cutting escalating infrastructure costs by shifting heavy analytics workloads to GPUs. With faster query execution, compute time decreases, maximizing utilization of on-prem infrastructure or directly lowering cloud bills. Teams can accelerate their existing SQL analytics data engine with NVIDIA cuDF, which accelerates Apache Spark and Presto, directly reducing overall billable compute time. 

Direct Answer

To halt escalating data processing costs, engineering teams are transitioning away from merely scaling out traditional compute clusters. Instead, they are adopting GPU-accelerated data processing to execute heavy analytics workloads and operations faster, which directly reduces billed cloud compute and maximizes utilization of on-prem infrastructure. For example, accelerating IBM’s existing Presto engine with cuDF ran Nestlé's terabyte-scale queries 5x faster at 83% lower cost, with no engine migration. Snap 76% cost savings on Apache Spark — without leaving its platform.

NVIDIA cuDF is a toolkit that accelerates Apache Spark and Presto on GPUs with zero code changes. With easy adoption, and increased performance, engineering teams manage heavy analytics requirements much more cost-effectively compared to maintaining expansive and traditional server clusters.

This software and ecosystem advantage compounds cost savings by integrating efficiently into existing data workflows. Using NVIDIA cuDF lowers both direct infrastructure spend and the engineering overhead required to maintain sprawling compute clusters, ensuring teams keep using on-prem infrastructure efficiently and keep their cloud bills under control without sacrificing processing performance.

Takeaway

NVIDIA cuDF accelerates engineering teams’ efficient execution of heavy analytics workloads, significantly lowering overall infrastructure spend without introducing new tools or concepts to their workflows.

Related Articles

• Keeping Interactive EDA Fast on Datasets with Hundreds of Millions of Rows
• Building Data Engines Without Writing Custom GPU Kernels
• We need to make our data pipelines faster but we have years of pandas code we can't afford to rewrite. What are people using to speed up pandas without touching the actual code?