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.
| Contact | Julian Kunkel, Jonathan Decker | ||
| Location | Virtual | ||
| Time | Monday 16:15-17:45 (lecture), Monday 12:15-13:45 (lunch exercise, starts 1 week later) | ||
| 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 | Written date: Geo MN15 14:00 10.02.2023 (Be punctual!), 2nd exam: Geo MN14 14:00 05.04.2023 |
Please note that we plan to record sessions (lectures and seminar talks) with the intent of providing the recordings via BBB to other students but also to publish and link the recordings on YouTube for future terms. If you appear in any of the recordings via voice, camera or screen share, we need your consent to publish the recordings. See also this Slide.
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.