NDAK18001U  Big Data Systems (BDS)

Volume 2018/2019
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.

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.
Lectures, seminars and discussions.
Continuous feedback during the course of the semester
Credit
7,5 ECTS
Type of assessment
Continuous assessment
The exam consists of four elements:
- seminar presentations of selected topics
- written reviews of research articles
- group project during the course
- oral exam in the examination week based on the project (20 minutes including grading, without preparation)

In the final grade the weight of the different parts is as follows: seminar presentation and review of research articles: 50%, group project and the oral exam: 50%.
Aid
All aids allowed
Marking scale
7-point grading scale
Censorship form
No external censorship
Several internal examiners.
Re-exam

Resubmission of (possibly revised) course elements; elements not redone are carried over with their original assessments. Project reports and article reviews must be resubmitted no later than two weeks before the re-exam date; seminar must be held no later than one week before the re-exam date; a new oral exam (if required) will be administered on the re-exam date itself.

Criteria for exam assesment

See Learning Outcome.

  • Category
  • Hours
  • Exam
  • 24
  • Lectures
  • 42
  • Project work
  • 90
  • Seminar
  • 50
  • Total
  • 206