Advanced Big Data Analytics

GANPAT UNIVERSITY

FACULTY OF ENGINEERING & TECHNOLOGY

Programme

Bachelor of Technology

Branch/Spec.

Computer Science & Engineering (BDA)

Semester

VII

Version

1.0.0.0

Effective from Academic Year

2022–23

Effective for the batch Admitted in

June 2019

Subject code

2CSE711

Subject Name

ADVANCED BIG DATA ANALYTICS

Teaching scheme

Examination scheme (Marks)

(Per week)

Lecture

(DT)

Practical

(Lab.)

Total

CE

SEE

Total

L

TU

P

TW

Credit

3

0

1

0

4

Theory

40

60

100

Hours

3

0

2

0

5

Practical

30

20

50

Pre-requisites:

Data Science & Analytics, Big Data Analytics, Database Management System, HDFS and MapReduce

Learning Outcome:

After successful completion of the course students will be able to:

• Understand several key big data technologies used for storage, analysis and manipulation of data.
• Recognize the key concepts of MongoDB, Scala and Spark
• Solve business intelligence related queries using Data Visualization tool, i.e., tableau, PowerBI
• Apply Advance concepts related to big data in projects/real life scenarios

Theory syllabus

Unit

Content

Hrs

1

NOSQL Databases

Types and importance of NoSQL Databases, MongoDB CRUD operations, Aggregation Framework, indexes

15

2

Processing stream data

SCALA:

What is Scala? Basic Operations, variable types, control structure, foreach loop, functions, procedures, array, higher order functions, Class in Scala, getters and setters, constructor, singletons, traits

SPARK:

Spark Components & its Architecture, Spark Deployment Modes, Spark Resilient Distributed Dataset (RDDs), RDD operations, transformations and actions, data loading and saving, Key-Value Pair RDDs, RDD Persistence, SPARK SQL, dataframes and datasets, JSON and Parquet file formats

20

3

Data Visualization tools and techniques

Tableau:

Data type, file type, tool type, show me menu, Type of data source supported by, how to connect different data sources, edit metadata, filter fields, filter data source, type of charts, filter data, data joining, data blending, extract data, adding filter data, apply filter on chart and data, number functions, string functions.

Power BI:

Components of Power BI, designing tables and reports, preparing dashboards

10

Practical content

Practicals will be based on:

• MongoDB CRUD Operations
• MongoDB Aggregation Framework
• Tableau: Preparing Dashboards and Stories
• Spark: Multiple practicals on Scala and Spark and SPARK SQL (dealing with different types of data like mysql, csv, json, parquet, etc)

Text Books

1

Spark: The Definitive Guide by Bill Chambers and Matei Zaharia

2

MongoDB: The Definitive Guide by Shannon Bradshaw , Eoin Brazil

Reference Books

1

Scala in depth by Joshua D. Suereth

2

Tableau For Dummies by Molly Monsey and Paul Sochan

3

Introducing Microsoft Power BI, Alberto Ferrari and Marco Russo

Course Outcomes:

Cos

Description

CO1

Understand several key big data technologies used for storage, analysis and manipulation of data.

CO2

Recognize the key concepts of MongoDB, Scala and Spark

CO3

Solve business intelligence related queries using Data Visualization tool like tableau, PowerBI

CO4

Apply Advance concepts related to big data in projects/real life scenarios

Mapping of CO and PO: