Big Data EngineeringLaajuus (5 cr)
Code: TT00CN70
Credits
5 op
Objective
After completing the course the student can:
- describe basic solutions for data architectures and big data
- select and use suitable data architecture
- apply ETL process and tools for handling of big data
Content
Architecture and Components of Big Data Frameworks
ETL process with Big Data for batch and streaming
Practical work with suitable tools and frameworks
Enrollment
29.11.2023 - 18.01.2024
Timing
08.01.2024 - 30.04.2024
Number of ECTS credits allocated
5 op
Mode of delivery
Contact teaching
Unit
Engineering and Business
Campus
Kupittaa Campus
Teaching languages
- English
Seats
10 - 50
Degree programmes
- Degree Programme in Information and Communication Technology
- Degree Programme in Business Information Technology
- Degree Programme in Information and Communications Technology
Teachers
- Tommi Tuomola
Teacher in charge
Tommi Tuomola
Groups
-
PTIETS22deaiPTIETS22 Data Engineering and Artificial Intelligence
-
PTIVIS22IData Engineering and AI
Objective
After completing the course the student can:
- describe basic solutions for data architectures and big data
- select and use suitable data architecture
- apply ETL process and tools for handling of big data
Content
Architecture and Components of Big Data Frameworks
ETL process with Big Data for batch and streaming
Practical work with suitable tools and frameworks
Materials
Teacher provided lecture material
Supporting public online material
Teacher provided virtual machines
All needed material (or at least a link to them) will be available in itslearning.
Teaching methods
Contact learning, practical exercises, independent study
International connections
Given examples and exercises support each topic studied during the lectures. Additional material in the form of tutorials and reliable information sources is provided.
Student workload
Contact hours 56 h
Inpendent studying 79h, including:
- Studying the course material
- Completing exercises
- Project
Content scheduling
-The basic idea of data engineering methods and pipelines
-different components
-integration of said components (MQ systems)
-data engineering frameworks (Apache family)
-The goal of the course is to be able to build a data pipeline from start to finish
Further information
Itslearning and contact classes are the main communication channels used on this course.
The student is required to have a computer capable of running a simple Ubuntu virtual machine.
Evaluation scale
H-5
Assessment methods and criteria
Homework exercises returned throughout the course
Small project at the end of the course
Enrollment
02.12.2023 - 16.01.2024
Timing
01.01.2024 - 30.04.2024
Number of ECTS credits allocated
5 op
Mode of delivery
Contact teaching
Unit
Engineering and Business
Campus
Kupittaa Campus
Teaching languages
- English
Seats
20 - 40
Degree programmes
- Degree Programme in Information and Communication Technology
- Degree Programme in Information and Communications Technology
Teachers
- Tommi Tuomola
Teacher in charge
Tommi Tuomola
Groups
-
PTIVIS21HTerveysteknologia
Objective
After completing the course the student can:
- describe basic solutions for data architectures and big data
- select and use suitable data architecture
- apply ETL process and tools for handling of big data
Content
Architecture and Components of Big Data Frameworks
ETL process with Big Data for batch and streaming
Practical work with suitable tools and frameworks
Materials
Teacher provided lecture material
Supporting public online material
Teacher provided virtual machines
All needed material (or at least a link to them) will be available in itslearning.
Teaching methods
Contact learning, practical exercises, independent study
International connections
Given examples and exercises support each topic studied during the lectures. Additional material in the form of tutorials and reliable information sources is provided.
Student workload
Contact hours 56 h
Inpendent studying 79h, including:
- Studying the course material
- Completing exercises
- Project
Content scheduling
-Introduction to data engineering
-The basic idea of data engineering methods and pipelines
-different components
-integration of said components (MQ systems)
-data engineering frameworks (Apache family)
Further information
Itslearning and contact classes are the main communication channels used on this course.
The student is required to have a computer capable of running a simple Ubuntu virtual machine.
Evaluation scale
H-5
Assessment methods and criteria
Homework exercises returned throughout the course
Small project at the end of the course