The big data blast has given rise to a host of information technology software and tools and abilities that enable companies to manage, capture, and analyze large data sets of unstructured and structure data for result oriented insights and competitive success. But with this latest technology comes the challenge of keeping confidential information secure and private.
Big data that resides within a Hadoop environment contains sensitive confidential data such as bank account details financial information in the form credit card, corporate business, property information, personal confidential information, security information of clients and all.
Due to the confidential nature of all of data and the losses that can be done should it fall into the wrong hands, it is mandatory that it be protected from unauthorized access.
look at some general Hadoop security issues along with best practices to keep sensitive data protected and secure.
It wasn’t all that long ago that Hadoop in the enterprise was primarily deployed on-premise. As such, informative confidential data was safely confined in isolated clusters or data silos where security wasn’t a problem. But that fastly changed as Hadoop developed into Big Data as-a-Service (BDaaS), took to the cloud, and became surrounded by an ever-growing ecosystem of softwares and applications. And while these innovations have served to democratize data and bring Hadoop into the mainstream, they have also created new security concerns for organizations that now struggle to scale security in step with Hadoop’s rapid technological advances.
For many companies Hadoop has developed into an enterprise data platform. That poses new security challenges as data that was once siloed is brought together in a vast data lake and made accessible to a variety of users across the organization. Among these challenges are:
Clearly, today’s compnies face formidable security challenges. And the stakes regarding data security are being raised ever higher as sensitive healthcare data, personal retail customer data, smart phone data, and social media and sentiment data become more and more a part of the big data mix. It’s time for companies to reevaluate the protection and safety of their data in Hadoop and to reacquaint themselves with the below Hadoop security good practices.
1. Plan before you deploy – Big data protection strategies must be determined during the planning phase of the Hadoop deployment. Before moving any data into Hadoop it’s important to identify any confidential data elements, along with where those elements will reside in the hadoop system.
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