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Cloudera Collaborates with NVIDIA to Accelerate Data Analytics and AI in the Cloud
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April 19, 2021 News

 

Cloudera, the enterprise data cloud company, announced that Cloudera Data Platform (CDP) will integrate the RAPIDS Accelerator for Apache Spark 3.0. Deployed on NVIDIA computing platforms, the software enables enterprises to accelerate data pipelines and push the performance boundaries of data and machine learning (ML) workflows to drive faster AI adoption and deliver better business outcomes, without changing any code. With the release earlier this year of Applied ML Prototypes (AMPs) in CDP and the power of NVIDIA computing, customers like the IRS, Office for National Statistics UK, and Commerzbank, and DBS Bank can not only jumpstart fully packaged ML use cases, but also accelerate data processing and model training at a lower cost across any on-premises, public cloud, or hybrid cloud deployment.

Enterprise data engineers are utilising data sets on a magnitude and scale never seen before, such as transforming supply chain models, responding to increased levels of fraud, or developing new product lines. For data scientists, the bottlenecks created by massive amounts of data directly impact the cost and speed at which companies can train and operate models across the organisation. Cloudera and NVIDIA’s integration are expected to give enterprises the ability to quickly respond to emerging and ongoing business challenges and deliver insightful analytics.

“We need to be able to make accurate decisions at speed utilising vast swathes of data. That challenge is ever-evolving as data volumes and velocities continue to increase,” said Joe Ansaldi, IRS/Research Applied Analytics & Statistics Division (RAAS)/Technical Branch Chief. “The Cloudera and NVIDIA integration will empower us to use data-driven insights to power mission-critical use cases such as fraud detection. We are currently implementing this integration, and are already seeing over three times speed improvements for our data engineering and data science workflows.”

For every company struggling with massive data sets, an open-source GPU-accelerated data science pipeline means the difference between being able to train models or never being able to do them at all. Such a pipeline can directly empower an organisation’s ability to transform using artificial intelligence. GPU-accelerated Apache Spark 3 runs seamlessly on CDP, allowing organisations to support HPC, AI, and data science needs – from research to production – with a secure, scalable, and open platform for machine learning.

“With businesses increasingly relying on data to power their decisions, speed is key to realising the true transformational potential of AI. Deepening our existing integration with NVIDIA will give customers the boost they need to confidently navigate the storm of today’s data challenges and exponential data growth.” said Mark Micallef, Vice President of Asia Pacific and Japan, Cloudera. “CDP analytic experiences are purpose-built to break down silos and enable greater analytic capabilities across multi-cloud environments. Our customers will now be able to leverage our enterprise data cloud services to drive faster workflows and maintain their competitive edge.”

“Apache Spark is a cornerstone of the machine learning and data analytics pipelines enterprises rely on to remain competitive,” said Scott McClellan, Senior Director, Data Science Product Group at NVIDIA. “The processing power of NVIDIA-accelerated computing and Spark analytics running on Cloudera Data Platform provides the flexibility to meet deadlines when time is of the essence, and save on costs when the bottom line is most important.”

The Cloudera public cloud implementation of RAPIDS-accelerated Apache Spark 3.0 libraries is now generally available and the on-premises solution offered by Cloudera will be GA this summer.

The RAPIDS Accelerator for Apache Spark will be available in CDP Private Cloud this summer. NVIDIA and Cloudera will roll out additional accelerated offerings in CDP over time, starting with Accelerated Deep Learning and Machine Learning in CDP Public Cloud in May

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