Big Data Platforms and Techniques

Author(s):  
Salisu Musa Borodo ◽  
Siti Mariyam Shamsuddin ◽  
Shafaatunnur Hasan

Data is growing at unprecedented rate and has led to huge volume generated; the data sources include mobile, internet and sensors. This voluminous data is generated and updated at high velocity by batch and streaming platforms. This data is also varied along structured and unstructured types. This volume, velocity and variety of data led to the term big data. Big data has been premised to contain untapped knowledge, its exploration and exploitation is termed big data analytics. This literature reviewed platforms such as batch processing, real time processing and interactive analytics used in big data environments. Techniques used for big data are machine learning, Data Mining, Neural Network and Deep Learning. There are big data architecture offerings from Microsoft, IBM and National Institute of Standards and Technology. Big data potentials can transform economies and reduce running cost of institutions. Big data has challenges such as storage, computation, security and privacy

Author(s):  
Zongheng Yang ◽  
Badrish Chandramouli ◽  
Chi Wang ◽  
Johannes Gehrke ◽  
Yinan Li ◽  
...  

Author(s):  
Richard Kumaradjaja

This chapter describes data integration issues in big data analytics and proposes an integrated data integration framework for big data analytics. The main focus of this chapter is to address the issues of data integration from the architectural point of view. Addressing the issues of data integration from the architectural point of view will lead to a better understanding of the current situation and better construction of proposed solutions to those issues since architectural approach can give us a holistic and comprehensive view of the problems. The chapter also discusses future research directions of the proposed integrated data architecture framework.


Author(s):  
Richard Kumaradjaja

This paper describes data integration issues in big data analytics and proposes an integrated data integration framework for big data analytics. The main focus of this article is to address the issues of data integration from the architectural point of view. Addressing the issues of data integration from the architectural point of view will lead to a better understanding of the current situation and better able to construct proposed solutions to those issues since architectural approach can give us a holistic and comprehensive view of the problems. The paper also discusses about future research directions of the proposed integrated data architecture framework.


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