Data-driven science requires the handling of large volumes of data in a quick period of time. Executing efficient workflows is challenging for users but also for systems. This module introduces concepts, principles, tools, system architectures, techniques, and algorithms toward large-scale data analytics using distributed and parallel computing. We will investigate the state-of-the-art of processing data of workloads using solutions in High-Performance Computing and Big Data Analytics.
Note that the lecture will be given online. I will make a survey regarding the exercise and presumably offer hybrid attendance for the exercise.
| Contact | Julian Kunkel | ||
| Location | Virtual, meeting room | ||
| Time | Monday 16:15-17:45 (lecture), Monday 12:15-13:45 (lunch exercise!) | ||
| Language | English | ||
| Module | Modul B.Inf.1712: Vertiefung Hochleistungsrechnen, Module M.Inf.1236: High-Performance Data Analytics | ||
| SWS | 4 | ||
| Credits | 6 | ||
| Contact time | 56 hours | ||
| Independent study | 124 hours | ||
| Exam | 17.03. - 10:00 - 12:00 in room MN09 Geowissenschaften, second exam date: Friday 08.04.2022, 10-12 Uhr In Person, room MN09 (Geowissenschaften) |
Topics cover:
Guest talks from academia and industry will be incorporated in teaching that demonstrates the applicability of this topic.
Weekly laboratory practicals and tutorials will guide students to learn the concepts and tools. In the process of learning, students will form a learning community and integrate peer learning into the practicals. Students will have opportunities to present their solutions to the challenging tasks in the class. Students will develop presentation skills and gain confidence in the topics.
Written (90 Min.) or oral (ca. 30 Min.) → depends on the number of attendees.
See the learning objectives.