The Critical Assessment on Big Data Analytics Course

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BIG DATA ANALYTICS

Semester

VII

Course Code

BCS714D

CIE Marks

Teaching Hours/Week (L:T:P: S)

3:0:0:0

SEE Marks

Total Hours of Pedagogy

Total Marks

100

Credits

Exam Hours

Examination nature (SEE)

Theory

Course objectives:

1. To implement MapReduce programs for processing big data.

2. To realize storage and processing of big data using MongoDB, Pig, Hive and Spark.

3. To analyze big data using machine learning techniques.

Teaching-Learning Process (General Instructions)

These are sample Strategies; that teachers can use to accelerate the attainment of the various course outcomes.

1. Lecturer method (L) needs not to be only a traditional lecture method, but alternative effective teaching

methods could be adopted to attain the outcomes.

2. Use of Video/Animation to explain functioning of various concepts.

3. Encourage collaborative (Group Learning) Learning in the class.

4. Ask at least three HOT (Higher order Thinking) questions in the class, which promotes critical thinking.

5. Discuss how every concept can be applied to the real world – and when that’s possible, it helps improve

the students’ understanding.

6. Use any of these methods: Chalk and board, Active Learning, Case Studies.

MODULE-1

Classification of data, Characteristics, Evolution and definition ofBig data, What is Big data, Why Bigdata,

Traditional Business Intelligence Vs Big Data,Typical data warehouse and Hadoop environment.

Big Data Analytics: What is Big data Analytics, Classification of Analytics, Importance of Big Data

Analytics, Technologies used in Big data Environments, Few Top Analytical Tools , NoSQL, Hadoop.

TB1: Ch 1: 1.1, Ch2: 2.1-2.5,2.7,2.9-2.11, Ch3: 3.2,3.5,3.8,3.12, Ch4: 4.1,4.2

MODULE-2

Introduction to Hadoop: Introducing hadoop, Why hadoop, Why not RDBMS, RDBMS Vs Hadoop, History

of Hadoop, Hadoop overview, Use case of Hadoop, HDFS (Hadoop Distributed File System),Processing data

with Hadoop, Managing resources and applications with Hadoop YARN(Yet Another Resource Negotiator).

Introduction to Map Reduce Programming: Introduction, Mapper, Reducer, Combiner, Partitioner,

Searching, Sorting, Compression.

TB1: Ch 5: 5.1-,5.8, 5.10-5.12, Ch 8: 8.1 – 8.8

MODULE-3

Introduction to MongoDB: What is MongoDB, Why MongoDB, Terms used in RDBMS and MongoDB, Data

Types in MongoDB, MongoDB Query Language.

TB1: Ch 6: 6.1-6.5

MODULE-4

Introduction to Hive: What is Hive, Hive Architecture, Hive data types, Hive file formats, Hive Query

Language (HQL), RC File implementation, User Defined Function (UDF).

Introduction to Pig: What is Pig, Anatomy of Pig, Pig on Hadoop, Pig Philosophy, Use case for Pig, Pig Latin

Overview, Data types in Pig, Running Pig, Execution Modes of Pig, HDFS Commands, Relational Operators,

Eval Function, Complex Data Types, Piggy Bank, User Defined Function, Pig Vs Hive.

TB1: Ch 9: 9.1-9.6,9.8, Ch 10: 10.1 – 10.15, 10.22

MODULE-5

Spark and Big Data Analytics: Spark, Introduction to Data Analysis with Spark

Suggested Learning Resources:

Books:

1.Seema Acharya and Subhashini Chellappan “Big data and Analytics”, Wiley India Publishers, 2nd Edition,

2019.

2.Rajkamal and Preeti Saxena, “Big Data Analytics, Introduction to Hadoop, Spark and Machine Learning”,

McGraw Hill Publication, 2019.

Reference Books:

1.Adam Shook and Donald Mine, “MapReduce Design Patterns: Building Effective Algorithms and Analytics for

Hadoop and Other Systems” – O’Reilly 2012

2.Tom White, “Hadoop: The Definitive Guide” 4thEdition, O’reilly Media, 2015.

3. Thomas Erl, Wajid Khattak, and Paul Buhler, Big Data Fundamentals: Concepts, Drivers & Techniques,

Pearson India Education Service Pvt. Ltd., 1st Edition, 2016

4. John D. Kelleher, Brian Mac Namee, Aoife D’Arcy -Fundamentals of Machine Learning for Predictive Data

Analytics: Algorithms, Worked Examples, MIT Press 2020, 2nd Edition

Web links and Video Lectures (e-Resources):

●https://www.youtube.com/watch?v=bAyrObl7TYE&list=PLEiEAq2VkUUJqp1k-g5W1mo37urJQOdCZ

● https://www.youtube.com/watch?v=VmO0QgPCbZY&list=PLEiEAq2VkUUJqp1kg5W1mo37urJQOdCZ&in

dex=4

●https://www.youtube.com/watch?v=GG-VRm6XnNk https://www.youtube.com/watch?v=JglO2Nv_92A

Activity Based Learning (Suggested Activities in Class)/ Practical Based learning

1. Implement MongoDB based application to store big data for data processing and analyzing the results [15

marks]

2. Install Hadoop and Implement the following file management such as Adding files and directories, Retrieving

files, Deleting files and directories and execute Map- Reduce based programs.[10

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