Five Pitfalls to Avoid with Hadoop

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Programming with the MapReduce processing paradigm in Hadoop requires not only Java programming skills, but also a deep understanding of how to develop the appropriate Mappers, Reducers, Partitioners, Combiners, etc. A typical Hadoop task often has multiple steps and a typical application can have multiple tasks. Most of these steps need to be coded by a Java developer (or using Pig script). With hand-coding, these steps can quickly become unwieldy to create and maintain.

Not only does MapReduce programming require specialized skills that are hard to find and expensive, hand-coding does not scale well in terms of job creation productivity, job re-use and job maintenance.

The emergence of Hadoop as the de facto Big Data operating system has brought on a flurry of beliefs and expectations that are sometimes simply untrue. Organizations embarking on their Hadoop journey face multiple pitfalls that, if not proactively addressed, will lead to wasted time, runaway expenditures and performance bottlenecks. By proactively anticipating these issues and utilizing smarter tools, the full potential of Hadoop may be realized. Syncsort has identified five pitfalls that should be avoided with Hadoop.


Related Topics : Vulnerabilities and Patches, Resellers, Broadcom, Broadband Services, Supercomputing

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