Program Overview

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ID2221 Data-Intensive Computing

The course ID2221 Data-Intensive Computing is a 7.5 credit course offered by the KTH Royal Institute of Technology.

Information per Course Offering

The course is offered in the autumn semester, with the following details:

• Start date: 24 August 2026
• End date: 23 October 2026
• Pace of study: 50%
• Application code: 10755
• Form of study: Normal Daytime
• Language of instruction: English
• Target group: Open to all programmes as long as it can be included in your programme

Part of Programme

This course is part of the following master's programmes:

• Master's Programme, Communication Systems, year 2, ITE
• Master's Programme, ICT Innovation, year 1, CLNH, Mandatory
• Master's Programme, Communication Systems, year 2, SMK
• Master's Programme, ICT Innovation, year 1, CLNC, Mandatory
• Master's Programme, Software Engineering of Distributed Systems, year 2, DASC
• Master's Programme, Communication Systems, year 2, TRN
• Master's Programme, Software Engineering of Distributed Systems, year 2, PVT
• Master's Programme, ICT Innovation, year 2, DASE, Mandatory
• Master's Programme, ICT Innovation, year 2, DASC, Mandatory
• Master's Programme, Machine Learning, year 2
• Master's Programme, Software Engineering of Distributed Systems, year 1, Mandatory
• Master's Programme, Machine Learning, year 1

Contact

• Examiner: Amir Hossein Payberah
• Course coordinator: Amir Hossein Payberah

Course Syllabus

The course syllabus is available as a PDF document.

Content and Learning Outcomes

Course Contents

The course covers the following topics:

• 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 main objective of this course is to provide students with a solid foundation for understanding large-scale distributed systems used for storing and processing massive data. After completing the course, students will be able to:

• Explain the architecture and properties of 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 implement non-trivial 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

Literature and Preparations

Specific Prerequisites

No specific prerequisites are listed.

Literature

Information about course literature can be found in the course memo or in the course room in Canvas.

Examination and Completion

Grading Scale

The grading scale for the course is A, B, C, D, E, FX, F.

Examination

The course examination consists of:

• TEN1 - Examination, 4.5 credits, grading scale: A, B, C, D, E, FX, F
• LAB1 - Programming Assignments, 3.0 credits, grading scale: P, F

Examiner

The examiner for the course is Amir Hossein Payberah.

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 Room in Canvas

Registered students can find further information about the implementation of the course in the course room in Canvas.

Offered by

The course is offered by the EECS/Computer Science department.

Main Field of Study

The main field of study for the course is Computer Science and Engineering.

Education Cycle

The course is part of the second cycle of education.

Supplementary Information

The EECS code of honor applies to this course.