MORABITO, Vincenzo. Big data and analytics: strategic and organizational impacts. Cham: Springer, [2015]. ISBN 978-3-319-10664-9.
SCIORE, Edward. Database design and implementation. Data-centric systems and applications. Cham, Switzerland: Springer, [2020]. ISBN 978-3-030-33835-0.
OLSZAK, Celina M. Business intelligence and big data: drivers of organizational success. Boca Raton: CRC Press, Taylor & Francis Group, 2020. ISBN 978-0-367-37394-8.
LAKSHMAN, Bulusu a ABELLERA, Rosendo. AI meets BI: artificial intelligence and business intelligence. Boca Raton: CRC Press, Taylor & Francis Group, [2021]. ISBN 978-0-367-33260-0.
Database Technologies
| Language of instruction | angličtina, čeština |
|---|---|
| Code | 545-0237 |
| Abbreviation | DBT |
| Course title | Database Technologies |
| Coordinating department | Department of Economics and Control Systems |
| Course coordinator | doc. Ing. Filip Beneš, Ph.D. |
Anotace
Students will acquire in-depth knowledge of database technologies and learn to apply theoretical concepts to the design, management, and optimization of database systems.
Professional Knowledge
The student defines and describes the principles, architectures, and models of relational and non-relational (NoSQL) database systems.
The student characterizes and compares technologies, methods, and tools designed for Big Data management and their deployment in cloud environments.
The student identifies and recognizes key mechanisms of indexing, query optimization, and security measures for protecting database systems.
The student lists and summarizes the application possibilities of databases in advanced analytical systems and data warehouses.
Professional Skills
The student constructs and designs conceptual, logical, and physical data models for relational and NoSQL platforms.
The student resolves and optimizes database query performance and implements security rules and access permissions.
The student converts, manages, and processes large-scale datasets within analytical and cloud systems.
The student classifies and selects an appropriate database engine based on specific application requirements for consistency, availability, and scalability.
General Competencies
The student explains and justifies the choice of a specific database architecture with regard to project security, operational, and scalability requirements.
The student discusses and anticipates the impact of architectural decisions on throughput, latency, and data system reliability.
The student independently researches, analyzes, and evaluates emerging trends in data management, cloud databases, and analytical platforms.
Professional Knowledge
The student defines and describes the principles, architectures, and models of relational and non-relational (NoSQL) database systems.
The student characterizes and compares technologies, methods, and tools designed for Big Data management and their deployment in cloud environments.
The student identifies and recognizes key mechanisms of indexing, query optimization, and security measures for protecting database systems.
The student lists and summarizes the application possibilities of databases in advanced analytical systems and data warehouses.
Professional Skills
The student constructs and designs conceptual, logical, and physical data models for relational and NoSQL platforms.
The student resolves and optimizes database query performance and implements security rules and access permissions.
The student converts, manages, and processes large-scale datasets within analytical and cloud systems.
The student classifies and selects an appropriate database engine based on specific application requirements for consistency, availability, and scalability.
General Competencies
The student explains and justifies the choice of a specific database architecture with regard to project security, operational, and scalability requirements.
The student discusses and anticipates the impact of architectural decisions on throughput, latency, and data system reliability.
The student independently researches, analyzes, and evaluates emerging trends in data management, cloud databases, and analytical platforms.
Povinná literatura
Doporučená literatura
DECKLER, Greg a POWELL, Bret. Mastering Microsoft Power BI: expert techniques to create interactive insights for effective data analytics and business intelligence. Second edition. Expert insight. Birmingham: Packt, [2022]. ISBN 978-1-80181-148-4.
SANTOS, Maribel Yasmina a COSTA, Carlos. Big data: concepts, warehousing, and analytics. River Publishers series in information science and technology. Gistrup, Denmark: Rever Publishers, [2020]. ISBN 978-87-7022-184-9.
SKYRIUS, Rimvydas. Business intelligence: a comprehensive approach to information needs, technologies and culture. Progress in IS. Cham, Switzerland: Springer, [2021]. ISBN 978-3-030-67031-3.
LABERGE, Robert. The data warehouse mentor: practical data warehouse and business intelligence insights. New York: McGraw-Hill, c2011. ISBN 978-0-07-174532-1 .
SANTOS, Maribel Yasmina a COSTA, Carlos. Big data: concepts, warehousing, and analytics. River Publishers series in information science and technology. Gistrup, Denmark: Rever Publishers, [2020]. ISBN 978-87-7022-184-9.
SKYRIUS, Rimvydas. Business intelligence: a comprehensive approach to information needs, technologies and culture. Progress in IS. Cham, Switzerland: Springer, [2021]. ISBN 978-3-030-67031-3.
LABERGE, Robert. The data warehouse mentor: practical data warehouse and business intelligence insights. New York: McGraw-Hill, c2011. ISBN 978-0-07-174532-1 .