TY  - BOOK
AU  - Kamal, Raj
AU  - Saxena, Preeti 
TI  - Big data analytics: introduction to hadoop, spark, and machine-learning 
SN  - 9789353164973
U1  - 005.74 
PY  - 2019///
CY  - Chennai :  
PB  - McGraw Hill Education
KW  - Electronic data processing
KW  - Business intelligence
KW  - Information technology
N1  - 1. Introduction to Big Data Analytics 
2. Introduction to Hadoop
3. NoSQL Big Data Management, MongoDB and Cassandra
4. MapReduce, Hive and Pig
5. Spark and Big Data Analytics 
6. Machine Learning Algorithms for Big Data Analytics 
7. Data Stream Mining and Real-Time Analytics Platform—SparkStreaming
8. Graph Analytics for Big Data and Spark GraphX Platform 
9. Text, Web Content, Link, and Social Network Analytics 
10. Programming Examples in Analytics and Machine Learning using Hadoop, Spark and Python

N2  - Big Data Analytics(BDA) is a rapidly evolving field that finds applications in many areas such as healthcare, medicine, advertising, marketing, and sales. This book dwells on all the aspects of Big Data Analytics and covers the subject in its entirety. It comprises several illustrations, sample codes, case studies and real-life analytics of datasets such as toys, chocolates, cars, and student’s GPAs. The book will serve the interests of undergraduate and post graduate students of computer science and engineering, information technology, and related disciplines. It will also be useful to software developers.
Highlights:
· Comprehensive coverage on Big Data NoSQL Column-family, Object and Graph databases, programming with open-source Big Data Hadoop and Spark ecosystem tools, such as MapReduce, Hive, Pig, Spark, Python, Mahout, Streaming, GraphX
· Inclusion of latest topics machine learning, K-NN, predictive-analytics, similar and frequent item sets, clustering, decision-tree, classifiers recommenders, real-time streaming data analytics, graph networks, text, web structure, web-links, social network analytics.
· Follows a hierarchical and teach-by- example approach from elementary to advanced level.
· Rich pedagogy
· Web supplement includes instructional PPTs, solution of exercises, analysis using open source datasets of a car company, and topics for advanced learning
ER  -