Issue Date:11/7/2021 issue:02
ZU/QP10F004
Faculty: Information Technology
Program: Bachelor Department: Artificial Intelligence
Semester:
Academic year:
Course Plan
First: Course Information
Credit Hours: 3 Course Title: Big Data
Course No.:
1505480
Lecture Time:
Section No.: 1 Prerequisite:
1501222
Obligatory Faculty Requirement Elective University Requirement Obligatory University Requirement Faculty Requirement
Course Elective Specialty Requirement Obligatory Specialization requiremen
tType Of Course:
Face-to-Face Learning
Blended Learning (2 Face-to-Face + 1Asynchronous) Online Learning (2 Synchronous+1 Asynchronous) Type of
Learning:
Second: Instructor’s Information
Academic Rank:
Name:
E-mail:
Ext. Number:
Office Number:
Thursday Wednesday
Tuesday Monday
Sunday Office Hours:
Third: Course Description
The aims of this course are to give students an in-depth understanding of Big Data concepts, application,
and platforms. This knowledge will enable the students to understand the need for big data, the
infrastructure needed for big data, the architecture of a big data system, the distributed file system HDFS,
the MapReduce programming platform, Batch analysis, and real time analysis and data streaming. This
course includes –also- NoSQL databases and Data visualization.
Issue Date:11/7/2021 issue:02
ZU/QP10F004
Fourth: Learning Source
Big Data Science and Analytics Main Reference:
Publication Year: 2019 Issue No.: 1
stedition
Authors: Arshdeep Bahga & Vijay Madisetti
www.hands-on-books-series.comAdditional Sources
&Websites:
Classroom Laboratory Workshop MS Teams Moodle Teaching Type:
Fifth: Learning Outcomes
Connection to Program ILOs Code
Course Intended Learning Outcomes (CILOs) Course Code
Knowledge
*PK1
Know the concept of Big Data and its origin
**K1
PK2
Know the architecture of a Big Data System
K2
Know the programming environment of the Big Data
PK3 K3
Know the Distributed File System in Big Data
PK4 K4
PK5
Know the batch and real-time processing within Big Data
K5
PK6
Know modern of databases used in Big Data
K6
PK7
Know new advancements of Big Data tools
K7
Skills
Application on the Hadoop platform
PS1
***S1
PS2
Application on the MapReduce programming
S2
Application on In-memory Spark system
PS3 S3
PS4
Application on HBase and other NoSQL databases.
S4
Application on MapReduce interfaces such as PIG
PS5 S5
PS6
Application on Storm real-time system
S6
PS7
Application on interactive Spark query
S7
Competencies
Use creative strategic thinking and innovation to mix different mathematical
PC1
models and propose efficient solutions for complex problems.
****C1
Communication: Express and communicate ideas in written and oral
PC2
forms
C2
PC3
Teamwork and Leadership: Be cooperative members of a team
C3
Organizational and Developmental Skills: plan, prioritize, and achieve
PC4
defined goals
C4
* P: Program, **K: knowledge, ***S: skills, ****C: competencies.
Issue Date:11/7/2021 issue:02
ZU/QP10F004
Sixth: Course Structure
References***
Teaching Methods**
Teaching Procedures*
Topics Intended Teaching
Outcomes (ILOs) Lecture
Date
Textbook Lecture,
In-class Questions Face-to-Face
Introduction to BigData K1, K2, S1
Week 1 Textbook,
slides Lecture,
In-class Questions Face-to-Face
Big Data platforms K1, K2
S1, S2, C2
Textbook, slides Lecture,
In-class Questions Face-to-Face
Hadoop Echo System K1, K2
S1, S2, C2
Textbook, slides Lecture,
In-class Questions Face-to-Face
Patch Processing and Real Time Processing
K1, K2 S1, S2, S3, C2
Week 2 Textbook,
slides Lecture,
In-class Questions Face-to-Face
Architectural Analytics (introduction)
K1, K2 S1, S2, C2
Textbook, slides Lecture,
In-class Questions Face-to-Face
Load Leveling and With Queues
K1, K2 S1, S2, C2
Textbook, slides Lecture,
In-class Questions Face-to-Face
Leader Election and Sharding K1, K2
S1, S2 C1,C3,C4
Week 3 Textbook,
slides Lecture,
In-class Questions Face-to-Face
Lambda Architecture K1, K2
S1, S2, C2
Textbook, slides Lecture,
In-class Questions Face-to-Face
Web Services K1, K2
S1, S2, C2
Textbook, slides Lecture,
In-class Questions Face-to-Face
MapReduce Patterns K1, K2, K3
S1, S2, S3, C2
Week 4 Textbook,
slides Lecture,
In-class Questions Face-to-Face
Web Analytics Example K1, K2, K3
S1, S2, S3, C2
Textbook, slides Lecture,
In-class Questions Face-to-Face
Sorting and Inverted Index Example
K1, K2, K3 S1, S2, S3, C2
Textbook, slides Lecture,
In-class Questions Face-to-Face
Join Examples K1, K2, K3
S1, S2, S3, C2, C1,C3,C4
Week 5 Textbook,
slides Lecture,
In-class Questions Face-to-Face
Big Data Storage K1, K2, K3, K4
S1, S2, S3, C2
Textbook, slides Lecture,
In-class Questions Face-to-Face
Hadoop Distributed File System HDFS
K1, K2, K3, K4 S1, S2, S3, S4, C2
Textbook, slides Lecture,
In-class Questions Face-to-Face
Name Node and Data Node K1, K2, K3, K4
S1, S2, S3, S4, C2,C1,C3,C4 Week 6
Textbook, slides Lecture,
In-class Questions Face-to-Face
HDFS Read/Write Path K1, K2, K3, K4
S1, S2, S3, S4 C2
Issue Date:11/7/2021 issue:02
ZU/QP10F004
Textbook, slides Lecture,
In-class Questions Face-to-Face
HDFS Examples K1, K2, K3, K4
S1, S2, S3, S4, C2
Textbook, slides Lecture,
In-class Questions Face-to-Face
NoSQL K1, K2, K3, K4
S1, S2, S3,S4, C2
Week 7 Textbook,
slides Lecture,
In-class Questions Face-to-Face
Key-Value Databases K3, K4, K5
S1, S2, S3,S4, C2
Textbook, slides Lecture,
In-class Questions Face-to-Face
Document Databases K3, K4, K5
S1, S2, S3, C2
Textbook, slides Lecture,
In-class Questions Face-to-Face
Column Family Databases K1, K2, K3, K4,
K5 S1, S2, S3,S4, S5,
C2 Week 8
Mid Exam
Textbook, slides Lecture,
In-class Questions Face-to-Face
Graph Databases K4, K5
S1, S2, S S4,S53, Week 9 C2
Textbook, slides Lecture,
In-class Questions Face-to-Face
NoSQL Database Example K3, K4, K5
S1, S2, S3, C2, C3,C4
Textbook, slides Lecture,
In-class Questions Face-to-Face
Patch Analysis Frameworks K1, K2, K3, K4,
K5 S1, S2, S3, S4,
S5, C2
Week 10 Textbook,
slides Lecture,
In-class Questions Face-to-Face
Hadoop and MapReduce K4,K5,K6
S5,S6 C2
Textbook, slides Lecture,
In-class Questions Face-to-Face
Apache Ooze K1, K2, K3,
K4,K5 S1, S2, S3,S4,S5
C2
Textbook, slides Lecture,
In-class Questions Face-to-Face
Apache Spark K4,K5
S4,S5,S6, C2
Week 11 Textbook,
slides Lecture,
In-class Questions Face-to-Face
Apache Solr K4, K5,K6
S4,S5,S6 C1, C3, C4
Textbook, slides Lecture,
In-class Questions Face-to-Face
K5, K6, K7 Pig S5, S6, S7, C2
Textbook, slides Lecture,
In-class Questions Face-to-Face
Pig Examples K5, K6
S5, S6, C1, C3, C4
Week 12 Textbook,
slides Lecture,
In-class Questions Face-to-Face
Real Time Analysis Framework K5, K6
S5, S6, C1, C3, C4
Textbook, slides Lecture, In-
class Questions Face-to-Face
Apache Storm K5, K6
S5, S6, C1, C3, C4
Textbook, slides Lecture,
In-class Questions Face-to-Face
Apache Spark K5, K7
S5, S7, C2 Week 13
Issue Date:11/7/2021 issue:02
ZU/QP10F004
Textbook, slides Lecture,
In-class Questions Face-to-Face
Spark Examples K5,K6,K7
S5,S6,S7, C2
Textbook, slides Lecture,
In-class Questions Face-to-Face
Interactive Query K5,K6,K7
S5, S6, S7, C2
Textbook, slides Lecture,
In-class Questions Face-to-Face
SparkSQL K5, K6, K7
S5, S6, S7, C2 Week 14
Textbook, slides Lecture,
In-class Questions Face-to-Face
Data Visualization K5, K6, K7
S5, S6, S7, C2
Review Face-to-Face
Final Exam
* Learning procedures: (Face-to-Face, synchronous, asynchronous). ** Teaching methods: (Lecture, video…). *** Reference:
(Pages of the book, recorded lecture, video…).
Seventh: Assessment methods
Methods Fully Electronic Education
Blended Learning
Face-to-Face Learning
Measurable Course (ILOs) First Exam
Second Exam
Mid Exam 0 0 30 K1, K2, K3, K4
S1, S2, S3, S4, S5, C2
Activities 0 0 20 K1, K2, K3, K4, K5
S4, S5, C1, C3, C4
Final Exam 0 0 50 K1, K2, K3, K4
S1, S2, S3, S4, S5, C2
Eighth: Course Policies
All course policies are applied to all teaching patterns (online, blended, and face- to-face Learning) as follows:
a. Punctuality.
b. Participation and interaction.
c. Attendance and exams.
Academic integrity: (cheating and plagiarism are prohibited).
