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Lecturer(s)
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Prokopová Zdenka, doc. Ing. CSc.
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Sysel Martin, doc. Ing. Ph.D.
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Šilhavý Petr, doc. Ing. Ph.D.
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Course content
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NoSQL databases - principles of NoSQL databases, types of NoSQL databases, representatives of NoSQL databases (MongoDB, HBase, etc.). Relational vs. NoSQL databases - advantages and disadvantages of relational and NoSQL databases, criteria for selecting a suitable database, i.e. database schema, data processing, database scalability, data consistency, and licensing policy. Business Intelligence - processing and analysis of large volumes of data in order to obtain information, respectively knowledge needed primarily for the decision-making process. Data warehouses, ETL process, methods of building data warehouses, OLAP process with explanation, and representation of multidimensional OLAP cube. Data mining selected methods and process scheme of data mining. Big Data - definition of Big Data and its distribution. Technologies for distributed data processing - Hadoop, HDFS, YARN, MapReduce, Hive, Sark, Impala.
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Learning activities and teaching methods
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Dialogic (Discussion, conversation, brainstorming), Methods for working with texts (Textbook, book), Individual work of students, E-learning
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| prerequisite |
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| Knowledge |
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| Knowledge from relational database systems. |
| Knowledge from relational database systems. |
| learning outcomes |
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| formulate requirements for a suitable database system |
| formulate requirements for a suitable database system |
| summarise the advantages and disadvantages of different database systems |
| summarise the advantages and disadvantages of different database systems |
| assess the suitability of a particular database system |
| assess the suitability of a particular database system |
| argue for the use of distributed data processing |
| argue for the use of distributed data processing |
| formulate requirements for Big data analysis |
| formulate requirements for Big data analysis |
| Skills |
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| plan the process of solving the problem of Big Data storage and manipulation |
| plan the process of solving the problem of Big Data storage and manipulation |
| compare the advantages and disadvantages of the design solutions |
| compare the advantages and disadvantages of the design solutions |
| construct a design for a selected database solution |
| construct a design for a selected database solution |
| implement distributed data processing |
| implement distributed data processing |
| design and implement a Big data analysis |
| design and implement a Big data analysis |
| teaching methods |
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| Knowledge |
|---|
| Dialogic (Discussion, conversation, brainstorming) |
| Individual work of students |
| Methods for working with texts (Textbook, book) |
| E-learning |
| Methods for working with texts (Textbook, book) |
| Dialogic (Discussion, conversation, brainstorming) |
| E-learning |
| Individual work of students |
| assessment methods |
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| Essay |
| Oral examination |
| Oral examination |
| Grade (Using a grade system) |
| Essay |
| Analysis of a presentation given by the student |
| Analysis of a presentation given by the student |
| Grade (Using a grade system) |
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Recommended literature
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CELKO, Joe. Joe Celko's analytics and OLAP in SQL.. San Francisco, 2006. ISBN 0-12- 369512-0.
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DEKA, Ganesh Chandra. NoSQL: database for storage and retrieval of data in cloud.. Boca Raton, 2017. ISBN 978-1498784368.
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ERL, Thomas, KHATTAK, Wajid. Big Data Fundamentals: Concepts Drivers: Con-cepts, Drivers and Techniques.. India, 2016. ISBN 978-933-257507-3.
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HARRISON, Guy. Next generation databases: NoSQL, NewSQL, and Big Data.. New York, 2015. ISBN 978-1-48421-330-8.
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HILLS, Ted. NoSQL and SQL data modeling.. Basking Ridge, NJ, 2016. ISBN 9781634621090.
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CHODOW, Kristina. MongoDB: The Definitive Guide.. 2013. ISBN 978-9-351-10269-4.
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MARZ, Nathan a James WARREN. Big data: principles and best practices of scalable real-time data systems.. New York, 2015. ISBN 978-1-61729-034-3.
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WHITE, Tom. Hadoop: the definitive guide.. Beijing, 2015. ISBN 978-1-491-90163-2.
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