* Designing Data-Intensive Applications, Martin Kleppmann, O'Reilly Media, 2017
* FAIR Data Principles — Wilkinson, M. D. et al. (2016), Scientific Data: https://www.nature.com/articles/sdata201618
* Demystifying Object-based Big Data Storage Systems, Anindita Sarkar Mondal, Madhupa Sanyal, Ari Kusumastuti, Hrishav Bakul Barua, Kartick Chandra Mondal, https://arxiv.org/abs/2406.00550
Distributed Data Management
| Language of instruction | angličtina |
|---|---|
| Code | 9600-1034 |
| Abbreviation | DSD |
| Course title | Distributed Data Management |
| Coordinating department | IT4Innovations |
| Course coordinator | Ing. Jan Martinovič, Ph.D. |
Summary
This course introduces the fundamentals of distributed data management and big data technologies. Topics covered include strategies for data distribution, partitioning and replication. Students will learn how to optimise data movement, manage metadata in accordance with the FAIR principles and develop automated, data-aware workflows. Through the analysis of real-world case studies students will develop the necessary skills to manage large-scale distributed data systems.
Literature
Advised literature
* M. van Steen and A.S. Tanenbaum, Distributed Systems, 3rd ed., distributed-systems.net, 2017
* iRODS documentation: https://irods.org/documentation/
* Introduction to Distributed Databases — TutorialsPoint, https://www.tutorialspoint.com/distributed_dbms/index.htm
* Nathan Marz and James Warren: Big Data - Principles and best practices of scalable realtime data systems, Manning, April 2015 ISBN 9781617290343 .
* iRODS documentation: https://irods.org/documentation/
* Introduction to Distributed Databases — TutorialsPoint, https://www.tutorialspoint.com/distributed_dbms/index.htm
* Nathan Marz and James Warren: Big Data - Principles and best practices of scalable realtime data systems, Manning, April 2015 ISBN 9781617290343 .