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ETL (Extract, Transform, and Load) and complex analytical queries are the unsung heroes of data-driven enterprises. Underperforming ETL and analytical workloads cause teams to miss their SLAs. Cloudera and NVIDIA have partnered to bring GPU acceleration for Apache Spark to enterprises everywhere, providing 20x performance improvement for complex ETL queries with Apache Spark 3.x. 

At this webinar, NVIDIA and Cloudera experts will cover:

  • How Cloudera Data Platform (CDP), powered by NVIDIA GPUs, turbocharges ETL and complex analytical query workloads for downstream data analytics and machine learning workloads.

  • How the Internal Revenue Service (IRS) uses data-driven insights to power mission-critical fraud detection use cases that were previously impossible, and experiences over 20X speed increases at half the cost for data engineering, ETL, and analytics workflows.

  • How you can achieve performance gains and exceed SLAs with zero code changes and easy to manage infrastructure that optimizes data center utilization today. 

  • How accelerated computing will enable your team to redefine “possible” for the problems that they solve tomorrow.

Speakers

Senior Product Marketing Manager

Varun Jaitly

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As Senior Product Marketing Manager, Varun leads the go-to-market strategy for Cloudera's Data Engineering and Operational Database products. Varun is currently based out of London and has over ten years of experience in driving product marketing for AI, SaaS and IaaS products, across both start-ups and large organizations.

Principal Product Architect

William Benton

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William Benton is passionate about making it easier for machine learning practitioners to benefit from advanced infrastructure and making it possible for organizations to manage machine learning systems. His recent roles have included defining product strategy and professional services offerings related to data science and machine learning, leading teams of data scientists and engineers, and contributing to many open source communities related to data, ML, and distributed systems. Will was an early advocate of building machine learning systems on Kubernetes and developed and popularized the “intelligent applications” idiom for machine learning systems in the cloud. He has also conducted research and development related to static program analysis, language runtimes, cluster configuration management, and music technology.

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