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

  • 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

  • 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

  • Design, implement, deploy and optimize Big Data systems.
  • Analyze 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.

Oral
Collective
Continuous feedback during the course of the semester
ECTS
7,5 ECTS
Type of assessment
Continuous assessment
The exam consists of two parts:
a. Group project assignments with individual defense in the exam week;
b. Oral exam in the exam week.

The individual project assignment defense 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: oral exam 50%, group project with individual defense: 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.

Single subject courses (day)

  • Category
  • Hours
  • Exam
  • 24
  • Lectures
  • 42
  • Project work
  • 90
  • Preparation
  • 50
  • English
  • 206

Kursusinformation

Language
English
Course number
NDAK18001U
ECTS
7,5 ECTS
Programme level
Full Degree Master
Duration

1 block

Schedulegroup
C
Capacity
50
Studyboard
Study Board of Mathematics and Computer Science
Contracting department
  • Department of Computer Science
Contracting faculty
  • Faculty of Science
Course Coordinator
  • Yongluan Zhou   (4-89777e844f73783d7a843d737a)
Teacher

Yongluan Zhou

Saved on the 12-06-2019

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