Big Data Security – Challenges and Recommendations

Abstract

This paper focuses on key insights of big data architecture which somehow lead to top 5 big data security risks and the use of top 5 best practices that should be considered while designing big data solution which can thereby surmount with these risks. Big data architecture, being distributive in nature can undergo partition, replication and distribution among thousands of data and processing nodes for distributed computation thus supporting multiple features associated with big data analytics like real time, streaming and continuous data computation along with massive parallel and powerful programming framework. These series of characteristics are put into effect via a key setup that somehow leads to certain crucial security implications. The challenges induced by this can be handled via big data technologies and solutions that exist inside big data architecture compound characterized for specific big data problems. Big data solutions should provide effective ways to be more proactive against fraud, management and consolidation of data, proper security against data intrusion, malicious attacks and many other fraudulent activities. In particular, this paper discusses the issues and key features that should be taken into consideration while undergoing development of secured big data solutions and technologies that will handle the risks and privacy concerns (e.g. Data security, insecure computation and data storage, invasive marketing etc.) associated with big data analysis in an effective way to increase the performance impact, considering that these risks are somehow a result of characteristics of big data architecture.