A Level - Syllabus of Module: A9.1-R5-Big Data Analytics Using Hadoop
Introduction :
The purpose of this module is to provide skills to students to analyze and process large
volume of data using tools and techniques. It provides theoretical background as well as
in-depth knowledge of Software/ packages that are used in analyzing the voluminous
data.
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| A Level - Syllabus of Module: A9.1-R5-Big Data Analytics Using Hadoop |
(i) Analyze and Define Business Requirement :
Introduction to Business Intelligence, Business Analytics, Data, Information, how
information hierarchy can be improved/introduced, understanding Business Analytics,
Introduction to OLAP, OLTP, data mining and data warehouse. Difference between
OLAP and OLTP.
Introduction to database, characteristics of data in database, DBMS, advantages of
DBMS, file-oriented approach versus Database-oriented approach to Data
Management, disadvantages of file- oriented approach. A brief overview of relational
model. Definition of relation, properties of relational model, Concept of keys:
candidate key, primary key, alternate key, foreign key, Fundamental integrity rules:
entity integrity, referential integrity. SQL statements: Insert, delete, update and select.
Join, union.
(ii) Introduction to Operating System :
Introduction to Ubuntu Operating System, Managing files and folder through
command line and Desktop. Basic Ubuntu commands like ls, mkdir, clear, rm. Creating
users and groups in Ubuntu. User priviledges and roles (chown and chmod commands),gedit editor. Secure shell configuration, configuring. bashrc and
environment files.
(iii) Java Programming :
OOPS Principles, an Overview of Java Object-Oriented Programming, Data Types,
Variables, and Arrays, Operators-Arithmetic Operators, The Bitwise Operators
,Relational Operators, Boolean ,Logical Operators, Programming Constructs,
Methods and Inheritance, The basic Java I/O Classes and String Handling
Exception-Handling Fundamentals, Exception Types, Uncaught Exceptions, Using
try and catch , Displaying a Description of an Exception ,Multiple catch Clauses ,
Nested try Statements , Throw throws finally Java’s Built-in Exceptions Packages,
Access Protection, Importing Packages and Interfaces
Java Swing and its controls like JTextField, JLabel, JComboBox, JTable, JButton,
JScrollBar, JOptionPane and JMenu.
Java Database Connectivity JDBC-ODBC Bridge JDBC Drivers Creating DSN
Driver Manager, Connection, Statement, Result Set. Connecting Java with Database.
(iv) Hadoop Framework and Map-Reduce Programming Technique :
Big Data Concepts, Need for analyzing Big Data, its roles in Business Intelligence
and decision making.
Big Data, Hadoop Architecture, Hadoop ecosystem components, storage, Hadoop
Distributed File System (HDFS), Single node installation. Multi node installations.
Cluster Architecture, Cluster configuration files Hadoop commands, Hadoop Server
Role, name Node, secondary node, data node, file write and read.
Shell commands, Accessing files on HDFS and local machine, Map Reduce
Framework, Developing Map Reduce Programs, structure of Map Reduce program,
(v) Analysing Data Using HIVE :
Introduction to HIVE, installing HIVE, Data types, HIVE shell, HIVE commands,
HIVE SQL, creating database and tables, bulk loading of data, SQL DML statements,
SQL Join, HIVE Functions, Complex Data types, UDF in Hive using Java
(vi) Basics of R Programming and RHIVE :
R Overview, Basic Syntax, Data types, R Control constructs like loop and
conditional, R Function. Connecting R with Hive.
(vii) HIVE JDBC Connectivity :
Starting HIVE in client-server mode, beeline, mapping HIVE datatype with Java
datatypes, Connecting Java with HIVE. Integrating Java Swing, HIVE and JDBC for
developing front end application.
(viii) Introduction to HBASE, PIG and JAQL : HBASE introduction, integration with Hadoop, HBase Shell, introduction to JAQL
data model, JAQL shell, introduction to JSON files and accessing JSON files through
JAQL. Introduction to PIG.
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