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FID3019 Advanced course in Data-Intensive Computing 7.5 credits

This course is a graduate reading course that will cover the research works of the recent years in the area of Data Intensive Computing. Every participant should find their own relevant research literature, read and analyze its contributions, give a presentation on the material and actively contribute to the group discussions, as well as write a short report on the paper.

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Choose semester and course offering to see current information and more about the course, such as course syllabus, study period, and application information.

Headings with content from the Course syllabus FID3019 (Spring 2019–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

• Distributed file systems
• No SQL databases
• Scalable messaging systems
• Big Data execution engines: Map-Reduce, Spark
• High level queries and interactive processing: Hive and Spark SQL
• Stream processing
• Graph processing
• Scalable machine learning
• Resource management

Intended learning outcomes

The course complements distributed systems courses, with a focus on processing, storing and analyzing massive data. It prepares the students for Ph.D. studies in the area of data-intensive computing systems.
The main objective of this course is to provide the students with a solid foundation for understanding large scale distributed systems used for storing and processing massive data.
More specifically after the course is completed the student will be able to:
• Explain the architecture and properties of the computer systems needed to store, search and index large volumes of data.
• Describe the different computational models for processing large data sets for data at rest (batch processing) and data in motion (stream processing).
• Use various computational engines to design and implements nontrivial analytics on massive data.
• Explain the different models for scheduling and resource allocation computational tasks on large computing clusters.
• Elaborate on the tradeoffs when designing efficient algorithms for processing massive data in a distributed computing setting.

Course disposition

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Literature and preparations

Specific prerequisites

Enrolled as a doctoral student.

Recommended prerequisites

Recommended prerequisites: Basic knowledge in distributed systems and programming models, programming languages (Scala, Java, Python)


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Examination and completion

If the course is discontinued, students may request to be examined during the following two academic years.

Grading scale

P, F


  • EXA1 - Examination, 7.5 credits, grading scale: P, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.


Other requirements for final grade

The course will be assessed with a Pass/Fail grade, based on a successful presentation, the delivery of a scientifically sound report, and the identification of appropriate papers for the reading list. In addition to this, students must attend at least 75% of all seminars.

Opportunity to complete the requirements via supplementary examination

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Opportunity to raise an approved grade via renewed examination

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Ethical approach

  • All members of a group are responsible for the group's work.
  • In any assessment, every student shall honestly disclose any help received and sources used.
  • In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

Further information

Course web

Further information about the course can be found on the Course web at the link below. Information on the Course web will later be moved to this site.

Course web FID3019

Offered by

Main field of study

This course does not belong to any Main field of study.

Education cycle

Third cycle

Add-on studies

No information inserted

Postgraduate course

Postgraduate courses at EECS/Software and Computer Systems