Big Data Engineering (5 cr)

Code: TT00CN70-3002

General information

Enrollment: 29.11.2023 - 18.01.2024
Registration for the implementation has ended.

Timing: 08.01.2024 - 30.04.2024
Implementation has ended.

Number of ECTS credits allocated: 5 cr

Local portion: 5 cr

Mode of delivery: Contact learning

Unit: Engineering and Business

Campus: Kupittaa Campus

Teaching languages: English

Seats: 10 - 50

Degree programmes: Degree Programme in Information and Communications Technology; Degree Programme in Business Information Technology; Degree Programme in Information and Communication Technology

Teachers: Tommi Tuomola

Teacher in charge: Tommi Tuomola

Groups: PTIETS22deai
PTIETS22 Data Engineering and Artificial Intelligence; PTIVIS22I
Data Engineering and AI

Course: TT00CN70

Realization has 4 reservations. Total duration of reservations is 8 h 0 min.

Time	Topic	Location
Fri 05.04.2024 time 10:00 - 12:00 (2 h 0 min)	Harjoitukset, Big Data Engineering TT00CN70-3002	ICT_C1032 Demotila
Tue 09.04.2024 time 09:00 - 11:00 (2 h 0 min)	Luento, Big Data Engineering TT00CN70-3002	ICT_C1032 Demotila
Fri 12.04.2024 time 10:00 - 12:00 (2 h 0 min)	Harjoitukset, Big Data Engineering TT00CN70-3002	ICT_C1032 Demotila
Fri 19.04.2024 time 10:00 - 12:00 (2 h 0 min)	Harjoitukset, Big Data Engineering TT00CN70-3002	ICT_C1032 Demotila

Changes to reservations may be possible.

Evaluation scale

H-5

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

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

Pedagogic approaches and sustainable development

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

Evaluation methods and criteria

Homework exercises returned throughout the course
Small project at the end of the course

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.