IOE Syllabus of Big Data Technologies

Big Data Technologies (Subject Code: CT 765 07) falls under Elective II for BE Computer and Electronics & Communication Engineering. Big Data Technologies has 3 lectures, I Tutorial and 3/2 Practical is elective for Fourth Year – Second Part. The Course Objectives of introducing Big Data Technologies is to introduce the current scenarios of big data and provide various facets of big data and to be familiar with the technologies playing key role in it and equips them with necessary knowledge to use them for solving various big data problems in different domains. Read: Notes, manuals and powerpoint slides of Big Data

1. Introduction to Big Data (7 hours)
   1. Big Data Overview
   2. Background of Data Analytics
   3. Role of Distributed System in Big Data
   4. Role of data Scientist
   5. Current Trend in Big Data Analytics
2. Google File System (7 hours)
   1. Architecture
   2. Availability
   3. Fault tolerance
   4. Optimization for large scale data
3. Map Framework (10 hours)
   1. Basics of functional programming
   2. Fundamentals of functional programming
   3. Real world problems modeling in functional style
   4. Map reduce fundamentals
   5. Data Flow (Architecture)
   6. Real world problems
   7. Scalability goal
   8. Fault tolerance
   9. Optimization and data locality
   10. Parallel Efficiency of Map-Reduce
4. NoSQL (6 hours)
   1. Structured and Unstructured Data
   2. Taxonomy and NoSQL Implementation
   3. Discussion of basic architecture of Hbase, Cassandra and MongoDb
5. Searching and Indexing Big Data
   1. Full text Indexing and Searching
   2. Indexing with Lucene
   3. Distributed Searching with Elastic search
6. Case Study Hadoop
   1. Introduction to Hadoop Environment
   2. Data Flow
   3. Hadoop I/O
   4. Query Languages for Hadoop
   5. Hadoop and Amazon Cloud

Practical:

Students will get opportunity to work in big data technologies using various dummy as well as real world problems that will cover all the aspects discussed in course. It will help them gain practical insights in knowing about problems faced and how to tackle them suing knowledge of tools learned in course.

1. HDFS: Setup a hdfs in a single node to multi node cluster, perform basic file system operation on it using commands provided, monitor cluster performance.
2. Map-Reduce: Write various MR programs dealing with different aspects of it as studied in course
3. Hbase: Setup of Hbase in single node and distributed mode, write program to write into hbase and query it.
4. Elastic Search: Setup elastic search in single mode and distributed mode, Define template, Write data in it and finally query it.
5. Final Assignment: A final assignment covering all aspect studied in order to demonstrate problem solving capability of students in big data scenario.

Evaluation Scheme:

The questions will cover all the chapters of the syllabus. The evaluation scheme will be as indicated in the table below:

*There could be a minor deviation in Marks distribution