Five Pitfalls That Can Derail Your Big Data Project

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Too many organizations looking for ways to break down data silos bring all the data together in one central place. While Hadoop is an excellent storage resource for large amounts of data (and it is in itself distributed across clusters), you need to think “distribution” beyond Hadoop. It’s not always necessary to duplicate and replicate everything. Some data is already readily available in the enterprise data warehouse, with fast, random access. Some of it might be better off residing where it was produced. The “logical data warehouse” concept applies well in the non-Big Data world. Leverage it for Big Data.

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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