LNMIIT, Jaipur
Department of Computer Science & Engineering
Programme: Course Title: Course Code:
B. Tech. CSE/CCE Introduction to Big Data
Type of Course: S L I Prerequisites: Ad m i s s i b i l it y to S LI Total Contact Hours:
Program Elective 2(L)+2(Lab) Equivalent
Year/Semester: 4 /8 Lecture Tutorial Hrs/Week: Average Practical Credits:
2 0 Hrs/Week: 2 2-0-2-3
Course Context and Overview:
Extreme data volume, velocity, and variety challenge conventional data-processing platforms and practices.
Big data discipline trades some advantages of the established approaches to surmount the limitations of
conventional storage infrastructures, data structures, databases and algorithms.
The course provides an understanding of the needs, purposes, and characteristics of the Big Data domain.
The students will gain an understanding of the platforms for executing big data applications, algorithms, and
analytical libraries.
Hadoop and Spark frameworks will guide the students in learning about the execution platforms that grow
linearly with the problem size. The students will also learn how these systems stay resilient and tolerant
against failures. The programming language Scala will be introduced as it provides the base for building
Apache Spark Analytical libraries. The libraries contain algorithms and techniques for solving big data
problems.
On successful completion, the students will be ready to continue learning big data tools, algorithms, and
libraries for handling Streaming data, NoSQL and SQL databases, Machine Learning, Frequent Pattern
Growth Algorithms, and Graph-based Analytics.
This subject is a hands-on self-study elective course requiring the students to demonstrate independent
learning, regular on-computer exercises and program implementations.
Prerequisites Courses:
Operating systems, Programming, Introduction to Data Sciences, Design and Analysis of Algorithms.
Course outcomes (COs):
On completion of this course, the students will have the ability to: Bloom taxonomy
Level
CO-1 Explain the purpose, concepts, and characteristics of big data applications 2
CO-2 Implement big data applications appropriate to the maturity level of an 3
undergraduate student.
CO-3 Explain multi-computer clusters available for big-data needs using 2
open-source software (for example, Hadoop and Apache Spark).
CO-4 Understand solutions using Analytics libraries available through 2
Apache Spark
CSE Department, LNMIIT Jaipur Page | 1-3
LNMIIT, Jaipur
Department of Computer Science & Engineering
Week Contents Topics Lectures + Labs
(Date of intro (Equivalent)
session) Lectures + Labs
Preliminary/Introduction
1 Meaning and implications of “big” in big data. Three Vs: 2 0
13 Jan 2024 Volume, Velocity, Variety. Other properties of big data.
2 Multi-computer processing. Java RMI. 2 4
20 Jan 2024
3 Examples of big data applications. 2
2
27 Jan 2024 Prepare and submit a big data proposal.
Hadoop Infrastructure
4, 5 Hadoop framework – HDFS, MapReduce paradigm, 5 0
3 & 10 Feb 2024 Combiner
6 Single-node Hadoop setup 1 6
17 Feb 2024
7 Running word count problem on Hadoop setup. 2 2
24 Feb 2024 Demonstration.
Midterm Examination
8 Fault-tolerance in Hadoop.
16 Mar 2024 Pseudo-distributed setup and word count problem
2 2
9 Frequent Item Set problem; approaches to run the 2 2
23 Mar 2024 algorithms as a big data exercise
10 Overview of YARN 2
0
30 Mar 2024
Resilience Distributed Datasets and SPARK
11 Introduction to Scala. Install Scala and practice some
2 2
6 April 2024 Scala code
12 Spark basis, Spark execution model, Install Spark and run 2 3
13 April 2024 examples
13 RDD, RDD Frames, RDD Sets
20 Apr 2024 2 0
14 Wrap-up report: Prepare the final report describing the 5
27 Apr 2024 lessons learned.
CSE Department, LNMIIT Jaipur Page | 2-3
LNMIIT, Jaipur
Department of Computer Science & Engineering
Textbook References (IEEE format) :
Text Book:
1. [BD] Rathinaraja Jeyaraj, Ganeshkumar Pugalendhi, Anand Paul, Big Data with Hadoop Map
Reduce: A Classroom Approach, Apple Academic Press,2020.
2. [DL] Doug Lea, Concurrent Programming in Java: Design Principles and Patterns, 2nd Edn, The
Java Series, Addison-Wesley, Boston, 2000.
3. [HADOOP] http://hadoop.apache.org/
4. [SPK] Bill Chambers and Matei Zaharia, SPARK: The Definitive Guide, O'Reilly Media, Inc,
2017.
5. [SCALA] https://www.scala-lang.org/
6. [SPARK] https://spark.apache.org/
7. [TW] Tom White, Hadoop: The Definitive Guide, 4th Edition, O’Reilley, 2015.
Evaluation Method:
Evaluation Method
Item Weightage (%)
Quiz (2) 12
Progress reports and Assigned Essays (3) 8+7+8=23
Midterm 25
Final Examination 40
CO and PO Correlation Matrix
For CSE Students
CO PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 PSO1 PSO2 PSO3
CO1 3 3 3 2 3 1 1 3 3 3 3 3 3 3
CO2 3 3 3 2 3 1 1 3 3 3 3 3 3 3
CO3 3 3 3 2 3 1 1 3 3 3 3 3 3 3
CO4 2 2 2 1 3 1 1 2 2 2 3 2 2 2
For CCE Students
CO PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 PSO1 PSO2 PSO3
CO1 3 3 3 2 3 1 1 3 3 3 3 1 2 3
CO2 3 3 3 2 3 1 1 3 3 3 3 1 2 3
CO3 3 3 3 2 3 1 1 3 3 3 3 2 2 3
CO4 2 2 2 1 3 1 1 2 2 2 3 2 2 2
Last Updated On: 20 December 2023
Updated By:
Approved By:
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