Big Data Systems (BDS)

Course content

The goal of this course is to give the participants an understanding of the technologies in computer systems for Big Data analysis and management. It covers both traditional methods used in data warehouses and parallel database systems, real-time stream processing systems, transactional database systems, as well as modern technologies of cloud computing and massively parallel data analysis platforms.

The following main topics are contained in the course:

  • Data warehouses;
  • Parallel database systems;
  • Massively parallel data analysis;
  • Fast stream processing systems;
  • Big graph processing;
  • High-throughput transaction processing;
  • Fault-tolerance;
  • Load balancing;
  • Elastic scaling;
  • Data partitioning.
Learning outcome

Knowledge of

  • Techniques in data warehouses and parallel database systems.
  • Techniques in data stream processing.
  • Theories behind massively parallel data analysis systems.
  • Design of and trade-offs in the modern systems introduced in the course.


Skills to

  • Develop programs and apply tools for big data management and analysis and deploy them on a cloud computing platform.
  • Report work done with Big Data systems in a clear and precise language, and in a structured fashion.


Competences to

  • Design, implement, deploy and optimise Big Data systems.
  • Analyse solutions in Big Data systems.
  • Discuss research articles related to Big Data systems with colleagues.
  • Plan and execute groups projects with Big Data systems and report the findings.

Lectures, seminars and discussions.

See Absalon when the course is set up.

The course builds on the knowledge acquired in the course NDAK15006U Advanced Computer Systems (ACS).
Working knowledge of Java, including concurrency and communication mechanisms.
Notions of UNIX / shell scripting are helpful, but not required.

Academic qualifications equivalent to a BSc degree is recommended.

Continuous feedback during the course of the semester
7,5 ECTS
Type of assessment
Continuous assessment
The assessment is based on the following two elements:

1. Group project assignments with individual defence in the exam week;
2. Oral examination in the exam week.

The individual project assignments defence and oral exam should be carried out in one session and should last for 30 minutes in total (including grading, without preparation).

In the final grade, the weight of the different parts is as follows:

1. Group project with individual defence: 50%.
2. Oral examination: 50%
All aids allowed
Marking scale
7-point grading scale
Censorship form
No external censorship
Several internal examiners.
Criteria for exam assessment

See Learning Outcome.

Single subject courses (day)

  • Category
  • Hours
  • Lectures
  • 28
  • Preparation
  • 50
  • Project work
  • 127
  • Exam
  • 1
  • English
  • 206


Course number
7,5 ECTS
Programme level
Full Degree Master

1 block

Block 2
Study Board of Mathematics and Computer Science
Contracting department
  • Department of Computer Science
Contracting faculty
  • Faculty of Science
Course Coordinator
  • Yongluan Zhou   (4-8270777d486c7136737d366c73)

Yongluan Zhou

Saved on the 01-03-2021

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