ITL25019 Big Data - Storage and Processing (Autumn 2019)
Facts about the course
- ECTS Credits:
- 10
- Responsible department:
- Faculty of Computer Science, Engineering and Economics
- Campus:
- Halden
- Course Leader:
- Marius Geitle
- Teaching language:
- See Forms of teaching and learning.
- Duration:
- ½ year
The course is connected to the following study programs
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 requirements
Knowledge equivalent to the course Database Systems /Databases.
Lecture Semester
5th semester (Autumn).
The student's learning outcomes after completing 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
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
Forms of teaching and learning
Lectures, project work and supervision.
If students from international partners attend courses, the lectures will be conducted in English
Workload
Approx. 250 hours.
Coursework requirements - conditions for taking the exam
Up to 4 mandatory exercises.
The coursework requirements must be approved before students may sit the exam.
Examination
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
The exam is assessed by the course instructor and an internal or external examiner.
Conditions for resit/rescheduled exams
In the event of a resit or rescheduled examination, all parts of the portfolio must be re-submitted.
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.
Literature
Literature is made available by June 1st 2019.