Companies are producing massive amounts of data—otherwise known as big data. There are many options available to manage big data and the analytics associated with it. One of the more popular options is Apache Hadoop, an open source software designed to scale up and down quickly with a high degree of fault tolerance. Hadoop lets organizations gather and examine large amounts of structured and unstructured data.
In the past, large CAPEX and deployment costs made large big data or Hadoop clusters cost prohibitive. Cloud providers, like IBM Cloud, have made it possible to break through the cost barriers. The cloud model, with its utility-type billing and usage charges, makes it possible to build big data clusters, use them for a specific project, then tear them down. IBM Cloud is a great solution for this type of scenario and makes sense for those that require short term or project-based Hadoop clusters. Hadoop on IBM Cloud allows organizations to respond faster to changing business needs and requirements without the upfront CAPEX.
What makes Hadoop on IBM Cloud so compelling are the components that are available in the IBM Cloud offering. Customers have the ability to choose and use the same type of components and standards that they would use in their own data centers. These components include bare metal servers, private and unmetered private networks, and enterprise-grade block and object storage. IBM Cloud also offers GPUs for the most processor-intense big data workloads. Customers don’t have to settle for less when deploying their Hadoop clusters in IBM Cloud.
Hadoop on IBM Cloud supports multiple data centers in different regions across the globe. The diagram below provides the graphical layout of Hadoop clusters across multiple IBM Cloud data centers.
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