Big Data Security Risk in the Enterprise: The Pitfalls of Hadoop

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As a data store, a Hadoop cluster is not a single physical entity, but consists of hundreds, sometimes thousands, of nodes.

Traditional data security technologies have been built on the concept of protecting a single physical entity (like a database or server), not the uniquely distributed Big Data computing environments characterized by Hadoop clusters. Traditional security technologies are not effective in this type of distributed, large-scale environment.

Side Note: The distributed nature of Hadoop clusters also renders many traditional backup and recovery methods and policies ineffective. Companies leveraging Hadoop need to replicate, back up and store data in a separate, secured environment.

Big Data wasn’t designed with security in mind; in fact, security has never really been the focus of distributed sciences. With these mountains of data, informing businesses on critical buyer decisions, habits, and countless other minutiae, comes a pressing need to keep this valuable information secure and protected. This is sensitive information, after all, and with so much of it comes a greater risk of breaches.

Data volumes are doubling annually, and roughly 80 percent of that captured data is unstructured, and must be formatted using a technology like Hadoop in order to be mineable for information. Considering this growth, it is clear that security concerns won’t be going away anytime soon. Quite the opposite, actually.

As Hadoop becomes more widely adopted in the enterprise, its security limitations are becoming more apparent. Brian Christian, co-founder and CTO of secure Big Data management vendor Zettaset, explains the biggest Big Data security challenges facing the enterprise today and his thoughts on creating a unified security model for Big Data.


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

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