Big Data Processing on Bare Metal Cloud with Apache Spark
How to automatically deploy a Spark cluster on non-virtual bare metal machines powered by Intel
White Paper by
Sergio Muriana and Seow Lim, phoenixNAP
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By 2025, it is estimated that the world will generate 463 exabytes of data each day, 80% of which will be unstructured.
Processing and contextualizing these massive datasets requires high-performance data processing technologies, including software and infrastructure solutions capable of delivering the necessary compute power on demand. As one of the leading software solutions for big data processing, Apache Spark is often used for optimizing big data workloads.
In this white paper, phoenixNAP’s experts explain how to automatically deploy an Apache Spark cluster on phoenixNAP’s Bare Metal Cloud Platform. Sergio Muriana, DevOps at phoenixNAP, and Seow Lim, Phd, VP of Architecture and Platform at phoenixNAP, provide an overview of Big Data processing trends and technologies, explaining how to leverage Apache Spark on Bare Metal Cloud to ensure efficiency of big data workloads.
The white paper covers:
- Big data processing challenges
- CPU, network, and storage requirements for big data processing
- Benefits of Apache Spark for data-intensive workloads
- Step-by-step instructions for deploying Apache Spark on Bare Metal Cloud
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