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Designing an Efficient Hadoop File System for Secured Big Data Management

   

Added on  2019-11-08

13 Pages2769 Words478 ViewsType: 478
Theoretical Computer ScienceData Science and Big DataMechanical Engineering
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IT RISK MANAGEMENT
Designing an Efficient Hadoop File System for Secured Big Data Management_1

IT risk managementTable of ContentsCase scenario:........................................................................................................................................2Vision:...................................................................................................................................................2Infrastructure of Big data security:........................................................................................................3Analysis of threats:................................................................................................................................5Significance:..........................................................................................................................................7Classification of threat agents:...............................................................................................................7Procedure for minimizing threats:.........................................................................................................8Trends and probability associated with the threats:...............................................................................8Deployment of Extract, transforms, and load process:.........................................................................10Evaluation and Recommendation:.......................................................................................................10Conclusion:..........................................................................................................................................11References:..........................................................................................................................................111
Designing an Efficient Hadoop File System for Secured Big Data Management_2

IT risk managementCase scenario:The ENISA is the development authority which focuses on streamlining the functionality of the processes, threat identification and mitigation policies, and organization of information security for the big data management program. The handling of the big data is equipped with some of the risks for which the control management program should be undertaken. The identification of the threats associated with the big data helps in developing mitigation plans to develop proactive action plan for the management of the big data over the network. Vision: The big data storage requires the deployment of high level replication strategies. “The inclusion of new threats focuses on the outsourcing of the big data management program” (Wang, 2014). The threats are associated with the data leakages and the degradation in losing the confidentiality of the system. The data protection and privacy methods are used for increasing the storage capability of the big data. The data collection method is used for specifying the key requirement for managing the big data. The management of the big data performance can be measured in terms of data leakages and associated breaches. The asset owners associated with the big data management are categorised as data owners, data transformers, computation providers, and storage service providers. The processes of the big data schemes are aligned with the help of coordination between the activities and conflicts. “The complexity of the data management processes can be resolved with the implementation of security schemes in the different phases of project life cycle” (Zhang, 2014). The large volumes of big data can affect the security measures which are undertaken for its management. The security principles and guidelines are used in the big data paradigm for reducing the security and privacy risks associated with the big data storage. The theory of gapanalysis is used for threat identification and develops new policies for mitigation. The mitigation policies are used for developing the flawless big data security infrastructure. The availability of the technology can result in the development of secure system for storing big data. “The processes which are equipped in the deployment of big data management are analysis of the traditional approach, threats identification, activities planning, deployment of the big data environment, security procedures, big data asset management, and identification of mitigation procedures” (Mobarak, 2016). The infrastructure provides different techniques to overcome the problem of threats associated with the management of the big data over the network. 2
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IT risk managementInfrastructure of Big data security: The analysis of the threat involves the amalgamation of different strategies for carrying out cyber security program. The gathering and collecting of information from different sources helps in the implementation of security infrastructure for the big data management. The functioning of the big data is adversely affected by the inclusion of the threats on the storage units. The interrelationship should be developed between different computation units such as power, storage, power, and analytics. The digital information should be collected from secured massive data on the internet. The procedures and policies should be developed for solving issues related with privacy and data protection. The 6V’s architecture is deployed for the development of big data management program which are described in the table below:6V’s ArchitectureDescriptionVolumeThe data should be collected and gathered from different sources from the internet. VelocityThe velocity focuses on the speed by which data packets aresends and received on the request of the user. VarietyThe different types of data can be collected from different sources which are available on the internet. VeracityThe quality of the data collected can be improved by making use of authentication tools and technologies. Variability“The scheduling of activities helps in overcoming the problem of inconsistencies which is associated with the dataarrival” (Terzi, 2015). The big data can effectively manage over the internet with the use of variability process. Value“The potential revenues can be collected with the values associated with the amalgamation of big data available on the internet” (Damiani, 2016). The following diagram shows the infrastructure of the big data management program:3
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