Big Data Parallel Programming

Education facts

Code: DT8034

Course objectives: Processing huge amounts of data is at the core of data mining, deep learning and real-time autonomous decision making. All these are in turn at the core of modern artificial intelligence applications. Data can reside more or less permanently in the cloud and accessed via distributed le systems and/or be streamed in real time from multiple sensors at very high rates. Access to data as well as processing is done using very well engineered frameworks where both storage and processing is done in parallel. The purpose of this course is to introduce you to this infrastructure including parallel programming for the implementation of these frameworks. This should enable you to judge how to choose a framework for your applications, identify pros and cons, suggest and even implement improvements. <br> Course content: The course includes modern techniques, methods and tools for distributed storage for static and streamed massive data, for example distributed and fault tolerant key-value tables including replication and coordination mechanisms. The course also includes modern techniques, methods and tools for distributed processing for static and streamed massive data including frameworks such as MapReduce and Spark. Finally, the course includes concepts, methods and tools for parallel programming for computing clusters, including GPUs.

Entry requirements

The course Edge Computing and Internet of Things 7.5 credits. English 6 or English level 2. Exemption of the requirement in Swedish is granted for those with foreign grades.

Education offerings

Each offering has its own dates and conditions. Closed offerings are retained as history and do not mean that a new application is open.

• Big Data Parallel Programming

  Halmstad University

  Halmstad

  Start date: 30 March 2026

  End date: 7 June 2026

  Pace of study: 50 %