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A Survey on Big Data privacy using Hadoop Architecture


Priyank Jain, Manasi Gyanchandani, Nilay Khare, Dhirendra Pratap Singh, Lokini Rajesh


Vol. 17  No. 2  pp. 148-155


Big Data is the term for any gathering of datasets so vast and complex that it gets to be distinctly troublesome to process using traditional data processing applications. The challenges include analysis, catch, curation, look, sharing, stockpiling, exchange, perception, and security infringement. Big data is a set of techniques and technologies that require new forms of integration to uncover huge concealed qualities from substantial datasets that are assorted, complex, and of a huge scale. Big data environment is used to acquire, organize and analyze the various types of data. Data that is so substantial in volume, so differing in assortment or moving with such speed is called big data. Analyzing Big Data is a challenging task as it involves large distributed file systems which should be fault tolerant, flexible and scalable. For such data-intensive applications, the Apache Hadoop Framework has recently attracted a lot of attention. This framework Adopted MapReduce, it is a programming model and a related execution for preparing and producing large data sets. The technologies used by big data application to handle the massive data are Hadoop, Map Reduce, Apache Hive, No SQL and HPCC. To begin with, we introduce the meaning of enormous information and discuss big data challenges. Hadoop is the core platform for structuring Big Data, and tackles the issue of making it helpful for examination purposes. Hadoop is an open source programming project that enables the distributed processing of large data sets across clusters of commodity servers. It is intended to scale up from a solitary server to a great many machines, with an extremely high degree of fault tolerance. This paper refer privacy and security aspects healthcare in big data. Next, we present Existing techniques of anonymization using MapReduce framework of big data privacy is also done as well.


Big Data, Hadoop, HDFS, MapReduce, Hadoop Components, Hive, NoSQL, Hpcc