Five Pitfalls That Can Derail Your Big Data Project

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Hadoop is not only a receptacle for Big Data with its distributed file system, but it is also an engine that brings incredible potential to process data and extract meaningful information. A broad ecosystem of tools and programming paradigms exist that cover all use cases of data manipulation. From MapReduce to YARN, from Pig to HiveQL complemented by Impala, Stinger or Drill, or through the merging of Hadoop and SQL engines like HAWK, there are processing resources available that make it unnecessary to get data out of the platform. All the resources are there, at your fingertips.

Most organizations are still in the early stages of Big Data adoption, and few have thought beyond the technology angle of how Big Data will profoundly impact their processes and their information architecture. Whether Big Data projects are past the pilot stage and being deployed in production, or still on the horizon, they require strategic thinking and adequate planning to avoid some now-typical pitfalls that tend to get in the way of success for big data projects.

Talend recently identified five key areas companies should monitor to avoid pitfalls that can derail big data projects, and ensure those projects generate value as they move past the early pilot stages.

 

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

 
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