Course

Unit 1

Introduction to Big Data: Types of Digital Data–Characteristics of Big Data – Evolution of Big Data – Definition of Big Data – Challenges with Big Data – 3Vs of Big Data. Big Data Analytics: Definition of big data analytics, Classification of analytics – Data Science – Terminologies in Big Data, Data science process – CAP and BASE Theorems.

Unit 2

Hadoop: Introduction – Hadoop distributions, RDBMS VS HADOOP – Hadoop Components – Architecture – HDFS, HDFS daemons, Name node, data node, File read and write operations, HDFS Commands. Hadoop 2 (YARN): Architecture –  Interacting with Hadoop Eco systems.

Unit 3

MapReduce: Introduction – Mapper – Reducer – Combiner – Partitioner – Searching–Sorting Compression.

Unit 4

NoSQL: Types of Databases, SQL vs. NOSQL. No SQL databases: Mongo DB: Introduction – Features –  Data types – Mongo DB Query language – CRUD operations – Functions: Count – Sort – Limit – Skip – Arrays–  Aggregate. Cassandra: Introduction – Features – Data types – CQLSH – Key spaces – CRUD operations – Collections – Counter – TTL – Alter commands

Unit 5

Hive:  Introduction – Architecture – data type – HQL (Database and Table Manipulation, DDL, DML, View, Joins), User defined functions. Pig: Features – Anatomy – Pig on Hadoop – Pig Philosophy – Pig Latin overview – Data types –  Running pig –  Execution modes of Pig – HDFS commands – Relational operators – Eval Functions – Complex data type – Piggy Bank – User defined Functions.

Text Books / References

Text Books / Reference Books:

1. Seema Acharya, Subhashini Chellappan, “Big Data and Analytics”, Wiley Publication, 2015.
2. Judith Hurwitz, Alan Nugent, Dr. Fern Halper, Marcia Kaufman, “Big Data for Dummies”, John Wiley & Sons, Inc., 2013.
3. Data Science and big data analytics: Discovering, analyzing, visualizing and presentation data, EMC Education Services, John Wiley 2015
4. Tom White, “Hadoop: The Definitive Guide”, O’Reilly Publications, 2011.
5. Kyle Banker, “Mongo DB in Action”, Manning Publications Company, 2012.
6. Russell Bradberry, Eric Blow, “Practical Cassandra a Developers Approach “, Pearson Education.

Course Objectives:

To provide students with a comprehensive understanding of Big Data concepts, technologies, and analytical frameworks, enabling them to store, process, manage, and analyze large-scale data using distributed computing platforms and modern data management tools.

Course Outcomes:

• CO1: Familiarize the concepts of Big Data
• CO2: Understanding the aspects of managing, cleaning and sampling of Data
• CO3: Understanding Hadoop architecture and implement Map Reduce concept
• CO4: Managing and querying No SQL databases
• CO5: Understanding and executing HDFS using PIG and HIVE

CO–PO Mapping: