ITL25019 Big Data - Storage and Processing (Autumn 2019)
Facts about the course
- ECTS Credits:
- 10
- Responsible faculty:
- Faculty of Computer Sciences, Engineering and Economics
- Campus:
- Halden
- Course Leader:
- Marius Geitle
- Teaching language:
- See Forms of teaching and learning.
- Duration:
- ½ year
Table of contents
The course is affiliated to the following study programme
This course is compulsory in
- Bachelor in Computer Science (2017)
- Bachelor in Information Systems (2017 and 2018)
- Bachelor in Information Systems - Software Engineering and Business Intelligence
Elective course for others.
Recommended previous knowledge
Teaching semester
Content
Applications and theory of
- important distributed file systems
- models for distributed computing such as Map Reduce
- important distributed and parallel algorithms
- distributed computing systems
The student's learning outcomes on passing the course
Knowledge
The student has
- knowledge of challenges with scalability, heterogeneity, security and error handling in distributed systems
- knowledge about how large quantities of data can be distributed across a large number of computers
- knowledge about the structure of distributed systems for Big Data
Skills
The candidate is able to
- design and implement solutions for distributed data storage and processing of large and distributed volumes of data
- describe, test and analyze the performance of distributed systems
General competence
The candidate can
- develop and use systems for storage and processing of Big Data
Teaching and learning methods
Lectures, project work and supervision.
If students from international partners attend courses, the lectures will be conducted in English
Workload
Coursework requirements - conditions for taking the exam
Up to 4 mandatory exercises.
The coursework requirements must be approved before students may sit the exam.
Exam
Individual portfolio assignment
The portfolio will include the following:
- a comparison of models on a selected problem
- a report describing the project
The portfolio is assessed as a whole and one overall grade is awarded.
Grading scale from A to F are used.
Examiners
Conditions for resit/rescheduled exams
Course evaluation
This course is evaluated by a
- Mid-term evaluation (compulsory)
The responsible for the course compiles a report based on the feedback from the students and his/her own experience with the course. The report is discussed by the study quality committee of the faculty of Computer Sciences.