School of Engineering \ Computer Engineering
Course Credit
ECTS Credit
Course Type
Instructional Language
Programs that can take the course
Computer Engineering
Artificial Intelligence Engineering
This course covers the fundamentals of big data processing and analysis; it answers the following questions: what is big data, how does it differ from other traditional data and traditional data processing, where is it used, and how is it used?"
Textbook and / or References
BigData Principles and Paradigms, Morgan Kaufmann, Elsevier
Equip students with a comprehensive understanding of distributed systems, data processing frameworks, and modern architectural paradigms to design and implement scalable, efficient, and reliable big data applications.
1. Know the concepts of Distributed File Systems
2. Know Distributed Data Processing Methods and Algorithms, and Data Pipeline Design
3. Know the concepts of Functional Programming
4. Know the principles of Distributed OLAP and OLTP Databases
5. Know the concepts of Virtualization at various levels
6. Know Processing on Stream Data, Lambda and Kappa Architectures
Week 1: Intro to Big data
Week 2: Big Data Concepts
Week 3: Principles of Distributed Storage
Week 4: Distributed Data Processing with MapReduce and DAG Design
Week 5: Functional Programming
Week 6: In Memory Distributed Data Processing with Apache Spark
Week 7: Join Strategies and Bloom Filters
Week 8: Distributed Database Concepts
Week 9: Distributed OLTP and OLAP Databases
Week 10: Levels of Paralellism and Virtualization Technologies
Week 11: Cluster Resource Management
Week 12: Data Pipeline Approaches, Lambda and Kappa Architectures
| Tentative Assesment Methods |
| Activities |
Number |
Weight (%) |
| Course Attendance/Participation |
- |
- |
| Laboratory |
- |
- |
| Application |
- |
- |
| Homework |
- |
- |
| Project |
1 |
30% |
| Presentation |
1 |
10% |
| Field Work |
- |
- |
| Internship |
- |
- |
| Course Boards |
- |
- |
| Quiz |
- |
- |
| Midterm Exam |
1 |
25% |
| Final Exam |
1 |
35% |
|
Total |
100% |
| Tentative ECTS-Workload Table |
| Activities |
Number/Weeks |
Duration (Hours) |
Workload |
| Course Hours (first 6 weeks) |
6 |
4 |
24 |
| Course Hours (last 6 weeks) |
6 |
4 |
24 |
| Laboratory |
- |
- |
- |
| Application |
- |
- |
- |
| Homework |
- |
- |
- |
| Project |
1 |
30 |
30 |
| Presentation |
1 |
15 |
15 |
| Field Work |
- |
- |
- |
| Internship |
- |
- |
- |
| Course Boards |
- |
- |
- |
| Preparation for Quiz |
- |
- |
- |
| Preparation for Midterm Exam |
1 |
20 |
20 |
| Final Exam |
1 |
2 |
2 |
| Preparation for Final Exam |
1 |
20 |
20 |
| Study Hours Out of Class (preliminary work, reinforcement, etc.) |
12 |
3 |
36 |
| Total Workload | | |
171 |
| Total Workload / 30 | | |
171 / 30 |
| | |
5.700000 |
| ECTS Credits of the Course | | |
6 |
|
Program Outcome
**
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2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
10 |
11 |
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Course Outcome
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| 1 |
C
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A
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B, A
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| 2 |
C
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A
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B, A
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| 3 |
C
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A
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A, B
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| 4 |
C
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A
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B, A
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| 5 |
C
|
A
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B, A
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| 6 |
C
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A
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B, A
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