TU Delft
Year
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NEDERLANDSENGLISH
Organization
2016/2017 Electrical Engineering, Mathematics and Computer Science Master Electrical Engineering
ET4310
Supercomputing for Big Data
ECTS: 5
Responsible Instructor
Name E-mail
Dr.ir. Z. Al-Ars    Z.Al-Ars@tudelft.nl
Contact Hours / Week x/x/x/x
2/0/0/0 college and 2/0/0/0 lab
Education Period
1
Start Education
1
Exam Period
none
Course Language
English
Course Contents
Big data is one of the hottest IT terms today, used to describe large and complex data sets that are difficult to process using traditional data processing systems. In this course, we will introduce the student to the most important concepts of big data and the available tools and systems used to manage it. In a series of labs, the students will be working with a number of different big data problems, addressing such aspects as implementing big data algorithms using Hadoop on a high performance computational system, as well as using higher-level languages and system management tools to guide and monitor big data systems. The students will have access to a big data cluster to evaluate the effectiveness of their implemented solution in practice.
Study Goals
By the end of this course the students will be able to
- implement big data algorithms using Hadoop and MapReduce
- use higher level languages such as Pig and AQL to simplify implementing more complex algorithms
- implement streaming, graph-based and parallel algorithms using the in-memory Spark framework
- identify the relevant characteristics of a given computational platform to solve big data problems
- utilize knowledge of hardware and software tools to produce an efficient implementation of the application
Education Method
Lab assignments and reading material provided via blackboard and online on the Big Data University site
Literature and Study Materials
Reading material provided via blackboard and the Big Data University
Assessment
The students will be evaluated based on the quality of their implementation and the report they submit describing their solution approach.