About author : Dr Poornima G. Naik bears twenty-two years of teaching and research experience in the field of Computer Science. She has been associated with teaching in thrust areas of computer science such as Big Data Analytics, Mobile Computing, Information System Security and Cryptography and her current research areas are Cyber Security, Machine Learning, Soft Computing and Big Data Analytics. She has guided three M Phil. Students. She has published more than 50 research papers in different national and international journals and presented more than 30 papers in different international and national conferences. She has authored 18 books on various cutting edge technologies in information technology. She is the recipient of prestigious Dr. APJ Abdul Kalam Life Time Achievement National Award for remarkable achievements in the field of Teaching, Research & Publications awarded by International Institute for Social and Economic Reforms, Bangalore. She has immense experience in guiding academic projects with computer aided tools. She has guided many industrial projects in project management, core banking solutions and e-Learning solutions.

About book : Big data analytics emerged as a revolution in the field of information technology. It is the ability of the organization to stay agile which gives it a competitive edge over its competitors. Data harvesting and data analytics enable the organization identify new opportunities which in turn results in efficient operations, leads to smarter business moves and higher business turnovers. All these issues are addressed by big data analytics and its initiatives. The book covers installation of hadoop on Ubuntu 16.06 LTS installed on VMware Workstation 15.0. The scope of this chapter is limited to installation of hadoop on a single cluster. Sqoop is a versatile tool, a popular framework for transferring data from/to hadoop (HDFS, HBase and Hive) to/from relational database management systems. Chapter 2 discusses sqoop workflow, working of sqoop and basic sqoop commands. Chapter 3 discusses at length a Hive tool which is an ETL (Extract, Load and Transform) and data warehouse system of hadoop. The salient features of this chapter are discussion of hive architecture, different deployment options for hive metastore, hive workflow, hive execution modes etc. among others.