Chapter 4. Cloud Infrastructure
In this chapter, Rich Morrow outlines the differentiators between the major cloud service providers—Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP)—as a guide for choosing where to launch a managed or unmanaged Hadoop cluster. Then Michael Li and Ariel M’ndange-Pfupfu compare AWS and GCP in terms of cost, performance, and runtime of a typical Spark workload. Finally, Arti Garg and Parviz Deyhim explore how tools like AWS Auto Scaling enable customers to automatically provision resources to meet real-time demand (i.e., scale up or scale down), leading to significant cost savings.
Where Should You Manage a Cloud-Based Hadoop Cluster?
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It’s no secret that Hadoop and public cloud play very nicely with each other. Rather than having to provision and maintain a set number of servers and expensive networking equipment in house, Hadoop clusters can be spun up in the cloud as a managed service, letting users pay only for what they use, only when they use it.
The scalability and per-workload customizability of public cloud is also unmatched. Rather than having one predefined set of servers (with a set amount of RAM, CPU, and network capability) in-house, public cloud offers the ability to stand up workload-specific clusters with varying amounts of those resources tailored for each workload. The access to “infinite” amounts of hardware that public cloud offers is also a ...
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