| Course Unit Code | 460-4070/01 |
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| Number of ECTS Credits Allocated | 4 ECTS credits |
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| Type of Course Unit * | Optional |
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| Level of Course Unit * | Second Cycle |
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| Year of Study * | First Year |
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| Semester when the Course Unit is delivered | Summer Semester |
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| Mode of Delivery | Face-to-face |
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| Language of Instruction | Czech |
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| Prerequisites and Co-Requisites | Course succeeds to compulsory courses of previous semester |
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| Name of Lecturer(s) | Personal ID | Name |
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| MOY001 | Ing. René Moyzes |
| Summary |
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The course is a follow-up to the Database and Information Systems 2 course with focus on applying the knowledge to the domain of Business Intelligence and Data Warehouse. The content of lectures is based on getting familiar with principles of Data Warehousing, data modelling specifics, design of respective layers of a data warehouse, data integration using SQL scripts and ETL tools, data transformations within respective layers of a data warehouse including final aggregations of data in order to be presented as business information in a graphical form and layout, or in a form of data extracts for further processing, where the second part of the course carries on in the next term. Another part of the course is the methodology for the solution design of a DWH and data integration projects. During practical sessions students will make use of the methodology in a practical example of data warehouse design and development in an SQL database environment within scope of their final work.
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| Learning Outcomes of the Course Unit |
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A student is able to orient in the domain of Business Intelligence and Data Warehousing (DWH), in particular practical knowledge of DWH data modeling methodology, ETL processes and data integration to data warehouses. Moreover, the student knows the methodology and he/she is able to create a reporting layer - data marts for analytics and reporting over data. The student is capable to describe the core basis of necessary data processing and operations with data in DWH database.
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| Course Contents |
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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
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| Recommended or Required Reading |
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| Required Reading: |
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| 1. L. T. Moss, Shaku Atre: Business Intelligence Roadmap: The Complete Project Lifecycle for Decision-Support Applications. 576p, Addison-Wesley Professional, 2003. |
1. D. Slánský, J. Pour, O. Novotný: Business Intelligence: Jak využít bohatství ve vašich datech. Grada, 256s, 2004.
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| Recommended Reading: |
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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.
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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.
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| Planned learning activities and teaching methods |
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| Lectures, Tutorials |
| Assesment methods and criteria |
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| Task Title | Task Type | Maximum Number of Points
(Act. for Subtasks) | Minimum Number of Points for Task Passing |
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| Graded credit | Graded credit | 100 | 51 |