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5 Expensive Traps of DIY Hadoop Big Data Environments

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Cloud Leader, Week of November 28, 2016 | Web View

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Editor's Note

Avoid These Mistakes
Some myths are rooted in truth—and myths about Apache Hadoop, the open source software framework for very large data sets, are no exception. Yes, Hadoop runs on cheap commodity computer hardware and it's easy for users to add nodes. But the devil is in the expensive details, especially when you're running Hadoop in a production environment, warns Jean-Pierre Dijcks, Oracle master product manager for big data.

Dijcks shares five common mistakes IT leaders make when choosing DIY Hadoop clusters (instead of, for instance, using a cloud-based big data service that takes care of complex Hadoop security, among other issues). Mistake #1: trying to do it on the cheap. "Hadoop is known to be self-healing, so if a node goes down on a server, it's not a problem," Dijcks says. But having cheap servers means more failures, "and when you have a chunk of nodes that aren't working, you've lost that capacity." Downtime and loss of capacity won't work if the experiment ends up in a production environment. Read about the other four mistakes, from having too many cooks to security oversights.

— Margaret Harrist, Oracle Director, Content Strategy and Implementation

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5 Expensive Traps of DIY Hadoop Big Data Environments

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