Big Data EngineeringLaajuus (5 op)
Tunnus: TT00CN70
Laajuus
5 op
Osaamistavoitteet
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
Sisältö
Architecture and Components of Big Data Frameworks
ETL process with Big Data for batch and streaming
Practical work with suitable tools and frameworks
Ilmoittautumisaika
04.12.2024 - 13.01.2025
Ajoitus
13.01.2025 - 30.04.2025
Opintopistemäärä
5 op
Toteutustapa
Lähiopetus
Yksikkö
Tekniikka ja liiketoiminta
Toimipiste
Kupittaan kampus
Opetuskielet
- Englanti
Paikat
0 - 80
Opettaja
- Tommi Tuomola
Ajoitusryhmät
- Ryhmä 1 (Koko: 35. Avoin AMK: 0.)
- Ryhmä 2 (Koko: 35. Avoin AMK: 0.)
Ryhmät
-
PTIETS23deaiData Engineering and Artificial Intelligence
-
PTIVIS23IData Engineering and Artificial Intelligence
Pienryhmät
- Ryhmä 1
- Ryhmä 2
Tavoitteet
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
Sisältö
Architecture and Components of Big Data Frameworks
ETL process with Big Data for batch and streaming
Practical work with suitable tools and frameworks
Oppimateriaalit
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.
Opetusmenetelmät
Contact learning, practical exercises, independent study
Tenttien ajankohdat ja uusintamahdollisuudet
There's no exam.
Pedagogiset toimintatavat ja kestävä kehitys
Given examples and exercises support each topic studied during the lectures. Additional material in the form of tutorials and reliable information sources is provided.
Opiskelijan ajankäyttö ja kuormitus
Contact hours 44 h
Independent studying 91h, including:
- Studying the course material
- Completing exercises
- Small Personal Project
Sisällön jaksotus
-The basic idea of big data engineering methods and pipelines
-different components and processes
-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 and to understand both the process and the different components and their role.
Viestintäkanava ja lisätietoja
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 and basic skills to work with Ubuntu command line.
Arviointiasteikko
H-5
Arviointimenetelmät ja arvioinnin perusteet
Homework exercises returned throughout the course
Small project at the end of the course
Arviointikriteerit, tyydyttävä (1-2)
Student has basic understanding of how the basic big data engineering processes work, what components the systems consist of and how they are used. The student has an idea of what can be done with big data engineering systems.
Arviointikriteerit, hyvä (3-4)
Student has a good understanding of big data engineering systems and processes. He is able to install many of the components and understands how they work together in a pipeline.
Arviointikriteerit, kiitettävä (5)
The student understands and is capable of designing big data engineering pipelines. He is able to install and configure the components and understands what kind of questions need to be considered when designing, deploying and implementing the system.
Ilmoittautumisaika
04.12.2024 - 13.01.2025
Ajoitus
13.01.2025 - 30.04.2025
Opintopistemäärä
5 op
Toteutustapa
Lähiopetus
Yksikkö
Tekniikka ja liiketoiminta
Toimipiste
Kupittaan kampus
Opetuskielet
- Englanti
Paikat
0 - 40
Opettaja
- Tommi Tuomola
Ryhmät
-
PTIVIS22HHealth Technology
Tavoitteet
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
Sisältö
Architecture and Components of Big Data Frameworks
ETL process with Big Data for batch and streaming
Practical work with suitable tools and frameworks
Oppimateriaalit
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.
Opetusmenetelmät
Contact learning, practical exercises, independent study
Tenttien ajankohdat ja uusintamahdollisuudet
There's no exam.
Pedagogiset toimintatavat ja kestävä kehitys
Given examples and exercises support each topic studied during the lectures. Additional material in the form of tutorials and reliable information sources is provided.
Opiskelijan ajankäyttö ja kuormitus
Contact hours 44 h
Independent studying 91h, including:
- Studying the course material
- Completing exercises
- Small Personal Project
Sisällön jaksotus
-The basic idea of big data engineering methods and pipelines
-different components and processes
-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 and to understand both the process and the different components and their role.
Viestintäkanava ja lisätietoja
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 and basic skills to work with Ubuntu command line.
Arviointiasteikko
H-5
Arviointimenetelmät ja arvioinnin perusteet
Homework exercises returned throughout the course
Small project at the end of the course
Arviointikriteerit, tyydyttävä (1-2)
Student has basic understanding of how the basic big data engineering processes work, what components the systems consist of and how they are used. The student has an idea of what can be done with big data engineering systems.
Arviointikriteerit, hyvä (3-4)
Student has a good understanding of big data engineering systems and processes. He is able to install many of the components and understands how they work together in a pipeline.
Arviointikriteerit, kiitettävä (5)
The student understands and is capable of designing big data engineering pipelines. He is able to install and configure the components and understands what kind of questions need to be considered when designing, deploying and implementing the system.
Ilmoittautumisaika
29.11.2023 - 18.01.2024
Ajoitus
08.01.2024 - 30.04.2024
Opintopistemäärä
5 op
Toteutustapa
Lähiopetus
Yksikkö
Tekniikka ja liiketoiminta
Toimipiste
Kupittaan kampus
Opetuskielet
- Englanti
Paikat
10 - 50
Koulutus
- Tieto- ja viestintätekniikan koulutus
- Tietojenkäsittelyn koulutus
- Degree Programme in Information and Communications Technology
Opettaja
- Tommi Tuomola
Vastuuopettaja
Tommi Tuomola
Ryhmät
-
PTIETS22deaiPTIETS22 Datatekniikka ja Tekoäly
-
PTIVIS22IData Engineering and AI
Tavoitteet
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
Sisältö
Architecture and Components of Big Data Frameworks
ETL process with Big Data for batch and streaming
Practical work with suitable tools and frameworks
Oppimateriaalit
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.
Opetusmenetelmät
Contact learning, practical exercises, independent study
Pedagogiset toimintatavat ja kestävä kehitys
Given examples and exercises support each topic studied during the lectures. Additional material in the form of tutorials and reliable information sources is provided.
Opiskelijan ajankäyttö ja kuormitus
Contact hours 56 h
Inpendent studying 79h, including:
- Studying the course material
- Completing exercises
- Project
Sisällön jaksotus
-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
Viestintäkanava ja lisätietoja
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.
Arviointiasteikko
H-5
Arviointimenetelmät ja arvioinnin perusteet
Homework exercises returned throughout the course
Small project at the end of the course
Ilmoittautumisaika
02.12.2023 - 16.01.2024
Ajoitus
01.01.2024 - 30.04.2024
Opintopistemäärä
5 op
Toteutustapa
Lähiopetus
Yksikkö
Tekniikka ja liiketoiminta
Toimipiste
Kupittaan kampus
Opetuskielet
- Englanti
Paikat
20 - 40
Koulutus
- Tieto- ja viestintätekniikan koulutus
- Degree Programme in Information and Communications Technology
Opettaja
- Tommi Tuomola
Vastuuopettaja
Tommi Tuomola
Ryhmät
-
PTIVIS21HTerveysteknologia
Tavoitteet
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
Sisältö
Architecture and Components of Big Data Frameworks
ETL process with Big Data for batch and streaming
Practical work with suitable tools and frameworks
Oppimateriaalit
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.
Opetusmenetelmät
Contact learning, practical exercises, independent study
Pedagogiset toimintatavat ja kestävä kehitys
Given examples and exercises support each topic studied during the lectures. Additional material in the form of tutorials and reliable information sources is provided.
Opiskelijan ajankäyttö ja kuormitus
Contact hours 56 h
Inpendent studying 79h, including:
- Studying the course material
- Completing exercises
- Project
Sisällön jaksotus
-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)
Viestintäkanava ja lisätietoja
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.
Arviointiasteikko
H-5
Arviointimenetelmät ja arvioinnin perusteet
Homework exercises returned throughout the course
Small project at the end of the course