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 management. It covers both traditional methods used in 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:

Parallel database systems;
Massively parallel data analysis;
Fast stream processing systems;
Distributed transaction processing;
Fault-tolerance;
Scalability;
Event-based systems.

Learning outcome

Knowledge of

Theories and techniques in parallel database systems.
Theories and techniques in data stream processing systems.
Theories and techniques in distributed transactional 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.

Teaching and learning methods

Lectures, seminars and discussions.

Literature

See Absalon when the course is set up.

Recommended prerequisites

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

Academic qualifications equivalent to a BSc degree is recommended.

Feedback form

Oral

Collective

Exam

ECTS: 7,5 ECTS
Type of assessment: Written assignment

Oral examination
Type of assessment details: The assessment is based on the following two elements:

1. Group project assignments (3-5) 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%
Aid: All aids allowed
Marking scale: 7-point grading scale
Censorship form: No external censorship
Several internal examiners.

Criteria for exam assessment

See Learning Outcome.

Course type

Single subject courses (day)

Workload

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

Kursusinformation

Language: English
Course number: NDAK18001U
ECTS: 7,5 ECTS
Programme level: Full Degree Master
Duration: 1 block
Placement: Block 2
Schedulegroup: C
Capacity: 50
The number of seats may be reduced in the late registration period
Studyboard: Study Board of Mathematics and Computer Science

Contracting department

Department of Computer Science

Contracting faculty

Faculty of Science

Course Coordinator

Yongluan Zhou (4-7f6d747a45696e33707a336970)

Teacher

Yongluan Zhou

Saved on the 28-02-2023

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Timetable

23E-B2-2;Hold 01;;Big Data Systems (BDS)

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