Big Data Processing

The term "Big Data" describes datasets that are either too big or change too fast or both to be processed on a single computer.

Big Data Processing provides an introduction to systems and algorithms used to process Big Data. The main focus of the course is programming and engineering big data systems; initially, the course explores general programming primitives that span across big data systems and touches upon distributed systems, databases and filesystems. Then, the course examines in detail the implementation of data analysis algorithms in Spark and Flink, in the context of batch, streaming, and graph processing applications.

The assessment is based on practical assignments (homework and lab sessions) as well as the final exam.

The assignments form 20% of the final grade and the exam 80%. The assignment grade is calculated as the mean grade of all assignments; non-submitted assignments are graded with 0.

To pass, the final grade must be 5.8 or higher AND the exam grade must be at least a 5 AND the assignment grade must be at least a 5. 

Resit en Repairs

The resit covers only the exam.

If the final assignment grade is between 4 and 5.7, one of the previously submitted assignments can be repaired.

Partial grades (assignments or the exam) cannot be transferred to subsequent years.

Disclaimer: information may change depending on unforeseen circumstances or measures (see: TER Art 29, sub 4).