Skip to main content
Skip header
Ukončeno v akademickém roce 2022/2023

Business Intelligence and Data Warehouses I

Type of study Follow-up Master
Language of instruction English
Code 460-4070/02
Abbreviation BI I
Course title Business Intelligence and Data Warehouses I
Credits 4
Coordinating department Department of Computer Science
Course coordinator prof. Ing. Michal Krátký, Ph.D.

Osnova předmětu

1. Introduction to BI and Data Warehousing
2. Data Warehouses (DWH) - Data modelling principles
3. Data Warehouses - multi-dimensional modelling, data layers - staging, main model, presentation layer (views, indexes).
4. Data Warehouses (DWH) - historization and SCD, using surrogate keys
5. Data marts
6. Operational Data Stores
7. Data Integration - ETL.
8. ETL Framework
9. Data Integration - ETL, transformation definitions - ETL development
10. ETL and Data Integration tools
11. Business Dictionaries and transformation rules
12. Data Quality
13. Analytics over DWH

Computer practices:
1. Practicals plan, platform introduction, sample databases.
2. Data modeling of tables and views in DWH.
3. Creation of DWH layers
4. Data historization, data indexing, surrogate keys creation
5. Fine tuning - querying (query definition for aggregations), analytical functions
6. Simple data pumps
7. Definition of ETL framework
8. Mappings definition, data workflow definition
9. ETL mappings - coding
10. ETL tool - Informatica
11. ETL tool - IBM DataStage/MS SSIS
12. Data profiling, Data cleansing - business rules definition
13. Test

Povinná literatura

1. L. T. Moss, Shaku Atre: Business Intelligence Roadmap: The Complete Project Lifecycle for Decision-Support Applications. 576p, Addison-Wesley Professional, 2003.

Doporučená literatura

1. R. Kimball, M. Ross: The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling. 600p, Wiley, 2013.
2. R. Kimball, J. Caserta: The Data Warehouse ETL Toolkit: Practical Techniques for Extracting, Cleaning, Conforming, and Delivering Data. 528p, Wiley, 2004.
3. C. Batini, M. Scannapieco: Data Quality: Concepts, Methodologies and Techniques (Data-Centric Systems and Applications). Springer, 2010.