Big Data Analytics

Course Objectives :

1. Understand the Big Data Platform and its Use cases
2. Provide HDFS Concepts and Interfacing with HDFS
3. Provide hands on Hadoop Eco System
4. Exposure to Data Analytics with R

Course Outcomes (CO)

1. CO 1 - Identify Big Data and its Business Implications
2. CO 2 - List the components of Hadoop and Hadoop Eco-System
3. CO 3 - Develop Big Data Solutions using Hadoop Eco System
4. CO 4 - Manage Job Execution in Hadoop Environment

UNIT-I

Introduction to Big Data: Introduction to Big Data, Big Data characteristics, Challenges of Conventional System, Types of Big Data, Intelligent data analysis, Traditional vs. Big Data business approach, Case Study of Big Data Solutions.

UNIT-II

Hadoop: History of Hadoop, Hadoop Distributed File System: Physical organization of Compte Nodes, Components of Hadoop Analyzing the Data with Hadoop, Scaling Out, Hadoop Streaming, Design of HDFS,Java interfaces to HDFS Basics, Developing a Map Reduce Application, How Map Reduce Works, Anatomy of a Map Reduce Job run, Failures, Job Scheduling, Shuffle and Sort, Task execution, Map Reduce Types and Formats, Map Reduce Features, Hadoop environment. Setting up a Hadoop Cluster, Cluster specification, Cluster Setup and Installation, Hadoop Configuration, security in Hadoop, Administering Hadoop, Monitoring-Maintenance, Hadoop benchmarks, Hadoop in the cloud

UNIT-III

NoSQL: What is NoSQL? NoSQL business drivers; NoSQL case studies; NoSQL data architecture patterns: Key-value stores, Graph stores, Column family (Bigtable) stores, Document stores, Variations of NoSQL architectural patterns; Using NoSQL to manage big data: What is a big data NoSQL solution? Understanding the types of big data problems; Analyzing big data with a shared-nothing architecture; Choosing distribution models: master-slave versus peer-to-peer; Four ways that NoSQL systems handle big data problems

UNIT – IV

Frameworks: Applications on Big Data Using Pig and Hive, Data processing operators in Pig, Hive services, HiveQL, Querying Data in Hive, fundamentals of HBase and ZooKeeper, IBM InfoSphere BigInsights and Streams. Machine Learning: Introduction, Supervised Learning, Unsupervised Learning, Collaborative Filtering. Big Data Analytics with BigR

Textbook(s):

1. Jiawei Han, Micheline Kamber, Jian Pei, “Data Mining : Concepts and Techniques”, 3rd edition, MK Publisher
2. Tom White “Hadoop: The Definitive Guide” Third Editon, O’reily Media, 2012.

References:

1. Seema Acharya, Subhasini Chellappan, "Big Data Analytics" Wiley 2015.
2. Michael Berthold, David J. Hand, "Intelligent Data Analysis”, Springer, 2007