AID402 Applied Data Engineering

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Course Code Course Title Weekly Hours* ECTS Weekly Class Schedule
AID402 Applied Data Engineering 3 2 6
Prerequisite None It is a prerequisite to


Lecturer Office Hours / Room / Phone

Currently not available

Assistant Assistant E-mail
Course Objectives • By the end of this course, attendees will gain comprehensive knowledge of the essential components of Big Data Analytics and its associated ecosystems.
• Participants will possess the skills to design, build, test, and sustain architectures such as databases and high-capacity processing systems. They will be capable of conducting batch and real-time streaming analytics on both structured and unstructured data, and demonstrate proficiency in professional data management. Additionally, they will be equipped to create visually appealing visualizations and dashboards.
• This course offers a thorough, progressive, and practical hands-on learning experience.
Textbook There is no required textbook.
Additional Literature
Learning Outcomes After successful  completion of the course, the student will be able to:
  1. Analyze the characteristics of data-intensive applications that rely on streaming data and propose appropriate solution architectures. This includes considering a combination of batch and streaming data processing techniques.
  2. Develop software applications that incorporate the analysis techniques and technologies covered in the course.
  3. Utilize frameworks for large-scale distributed machine learning in their data analysis and processing tasks.
Teaching Methods The course will begin with a one-hour theoretical overview, providing a thorough explanation of the topic's background. Following that, we will proceed to practical programming exercises and hands-on practice.
Teaching Method Delivery Face-to-face Teaching Method Delivery Notes
Week 1 Features of Data Engineering
Week 2 Metadata Management
Week 3 Consolidating Multiple Data Sources
Week 4 Data Ingestion, Cleansing and Transformation
Week 5 Hadoop Architecture and Ecosystem
Week 6 Flat Files Ingestion into Hadoop
Week 7 RDBMS and Hadoop Integration
Week 8 Hive Data Processing
Week 9 Interactive Query using Impala
Week 10 Log Files Handling and Processing
Week 11 Introduction to Spark
Week 12 Processing Data using PySpark
Week 13 Spark Data Query
Week 14 Real-time Data Analytics in Spark Streaming
Week 15
Assessment Methods and Criteria Evaluation Tool Quantity Weight Alignment with LOs
Final Exam 1 30
Semester Evaluation Components
Midterm 1 25
Quizzes 3 15
Term project and presentation 1 15
Lab assignments 7 15
***     ECTS Credit Calculation     ***
 Activity Hours Weeks Student Workload Hours Activity Hours Weeks Student Workload Hours
Lecture hours 3 14 42 Assignments 3 7 21
Active labs 2 14 28 Home study 1 14 14
In-term exam study 10 1 10 Final exam study 11 1 11
Term project/presentation 2 12 24
        Total Workload Hours = 150
*T= Teaching, P= Practice ECTS Credit = 6
Course Academic Quality Assurance: Semester Student Survey Last Update Date: 01/09/2023
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