INMBPDDS Databases and Data Warehouses

School of Business Administration in Karvina
Winter 2026
Extent and Intensity
1/2/0. 5 credit(s). Type of Completion: zk (examination).
Teacher(s)
RNDr. Zdeněk Franěk, Ph.D. (lecturer)
Mgr. Milena Janáková, Ph.D. (lecturer)
Guaranteed by
Mgr. Milena Janáková, Ph.D.
Department of Informatics and Mathematics – School of Business Administration in Karvina
Contact Person: doc. Ing. Jan Górecki, Ph.D.
Timetable
Wed 9:45–10:30 A412
  • Timetable of Seminar Groups:
INMBPDDS/01: Wed 10:35–12:10 A412, Z. Franěk, M. Janáková
Prerequisites (in Czech)
FAKULTA(OPF) && TYP_STUDIA(B) && FORMA(P)
Course Enrolment Limitations
The course is only offered to the students of the study fields the course is directly associated with.

The capacity limit for the course is 45 student(s).
Current registration and enrolment status: enrolled: 37/45, only registered: 0/45
fields of study / plans the course is directly associated with
Course objectives
The aim of the course is to acquire practical skills and knowledge for databases using model examples in a selected database environment. The subject of interest is also data warehouses and practical planning of advanced analyses and OLAP analyses.
Learning outcomes
The student can demonstrate practical knowledge of database management with an extension to data warehouses. Specifically, he/she explains the importance of databases, DBMS and formulates requirements for database processing. He/she actively proposes a database creation procedure and specifies the degree of variability for practical use on a model example. He/she characterizes the properties of relational tables, distinguishes between different keys of a relation (tables) and relationships between tables. From a practical point of view, he/she specifies the requirements for integrity, normalization and practices the syntax of selected SQL commands. He/she also combines automation options, designs and supplements implementation variants for model examples in a selected database environment. He/she identifies data warehouse sources, dimension and fact tables, ETL processes, practically plans advanced analyses and OLAP analyses. He/she formulates the influence of sustainability and circular economy on data warehouse database applications.
Syllabus

1. Introduction to databases and basic concepts
Database, database technology, definition of database structures. Database processing, logical and physical view of data. Multi-user access to the database. Access rights. Data definition language, data manipulation language and query languages. Modular architecture of the database system. User communication with the database.
2. Database management system and database application
Software system for defining and creating a database, controlled access to the database. DBMS (Database Management System) architecture. Three-tier ANSI - SPARC architecture. Database design. Database application, views, schemas and instances.
3. Relational model
Data model, structural part, manipulation part and set of integrity rules. Relational data model, relation, attribute, data tuple, domain, relational database. Properties of relational tables, keys, superkey, candidate key, primary key, alternate key and foreign key. Normalization. Relational integrity, entity integrity, referential integrity and other integrity constraints.
4. Relational and higher-level languages
Relational algebra and relational calculus. Higher-level languages, SQL (Structured Query Language) and QBE (Query-By-Example). Database objects (schemas, tables and indexes) and DDL (Data Definition Language). DML (Data Manipulation Language) and data manipulation commands. Stored procedures, transactions and triggers.
5. Database system development life cycle and automation support
Software application requirements, structured approach to software development. Database development life cycle. Database planning, system definition, requirements collection and analysis, database design, DBMS selection, application design, implementation, testing and operational maintenance. Agile approaches. Automation support and application of artificial intelligence. Big data.
6. Databases and data warehouses
Transition from relational databases to multidimensional ones. The need for analytical databases. Data warehouse and data market. Methods of building a data warehouse. Data preparation and ETL processes (Extraction, Transformation, Loading). Facts and dimensions, dimension table schemas.
7. OLAP (Online Analytical Processing) analysis and other analytical tools
Multidimensional cubes. Classic dimensions time, location and product. Creating an OLAP cube. Advanced analyses from different perspectives, data mining methods using models. Advantages of specialized software focused on data warehouses using dashboards compared to classic office software.

Literature
    required literature
  • Vystavěl, R. (2023). Myslete databázově, myslete v SQL! moderníProgramování.
  • Ganesan, CH. (2025). Fundamentals of Database Systems: Concepts, Design, and Applications. Pandit Publications.
  • Laurenčík, M. (2018). SQL Podrobný průvodce uživatele. Grada.
  • Kroenke, D. M., & Auer, D. J. (2015). Databáze. Computer Press.
    recommended literature
  • Rigdon, J. (2024). Databases: System Concepts, Designs, Management, and Implementation. Freegulls Publishing House LLC.
  • Vystavěl, R. (2021). Databáze a SQL pro začátečníky. moderníProgramování.
  • Malik, U., Goldwasser, M., & Johnston, B. (2019). FormSQL For Data Analytics: Perform fast and efficient data analysis with the power of SQL. Packt Publishing.
  • Kosek, J. (2015). Big data a NoSQL databáze. Grada.
  • Laberge, R. (2012). Datové sklady – agilní metody a business intelligence. Computer Press.
Teaching methods
Frontal teaching with activation elements, work with a professional text, research on the topic, interview and brainstorming, work in groups (also individually), practical examples with the application of selected procedures. Through team work, students are guided towards cooperative learning.
Assessment methods
active participation in seminars, midterm test, semester project, exam
Language of instruction
Czech
Teacher's information

Student requirements: active participation in seminars.
Assessment methods: 1 midterm test (week 13, questions from theory and database management) and a semester project.

Exam (demonstration of practical knowledge of database management with an extension to data warehouses). A student can get 8 points from the midterm test, 8 points for a semester project, 5 points for an activity and 14 points from the exam. To successfully complete the course, a student must get at least 21 points and a total of 35 points can be obtained.
Further comments (probably available only in Czech)
Study Materials
The course is also listed under the following terms Winter 2021, Winter 2023, Winter 2024, Winter 2025.
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