Ten Best Practices for Securing Sensitive Data in Hadoop

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Determine whether a tailored protection for specific data sets is required and consider dividing Hadoop directories into smaller groups where security can be managed as a unit.

Dataguise, a leading innovator of data security intelligence and protection solutions, recently released 10 security best practices for organizations considering or implementing Hadoop. By following these procedures to manage privacy risk, data management and security, professionals can prevent costly exposure of sensitive data, reduce their risk profile and better adhere to compliance mandates. With Hadoop security deployments among the Fortune 200, Dataguise has developed these practices and procedures from significant experience in securing these large and diverse environments.

The explosion in information technology tools and capabilities has enabled advanced analytics using Big Data. However, the benefits of this new technology area are often coupled with data privacy issues. In these large information repositories, personally identifiable information (PII), such as names, addresses and social security numbers may exist. Financial data such as credit card and account numbers might also be found in large volumes across these environments and pose serious concerns related to access. Through careful planning, testing, pre-production preparation and the appropriate use of technology, much of these concerns can be alleviated.

The following 10 Hadoop security best practices provide valuable guidance throughout Hadoop project implementations, but are especially important in the early planning stages.

 

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

 
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