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Computational tasks for Parallel data processing course
Horečný, Peter ; Rajnoha, Martin (referee) ; Mašek, Jan (advisor)
The goal of this thesis was to create laboratory excercises for subject „Parallel data processing“, which will introduce options and capabilities of Apache Spark technology to the students. The excercises focus on work with basic operations and data preprocessing, work with concepts and algorithms of machine learning. By following the instructions, the students will solve real world situations problems by using algorithms for linear regression, classification, clustering and frequent patterns. This will show them the real usage and advantages of Spark. As an input data, there will be databases of czech and slovak companies with a lot of information provided, which need to be prepared, filtered and sorted for next processing in the first excercise. The students will also get known with functional programming, because the are not whole programs in excercises, but just the pieces of instructions, which are not repeated in the following excercises. They will get a comprehensive overview about possibilities of Spark by getting over all the excercices.

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