460-4138/02 – Business Intelligence (BI)
| Gurantor department | Department of Computer Science | Credits | 4 |
| Subject guarantor | prof. Ing. Michal Krátký, Ph.D. | Subject version guarantor | prof. Ing. Michal Krátký, Ph.D. |
| Study level | undergraduate or graduate | Requirement | Choice-compulsory type B |
| Year | 2 | Semester | summer |
| | Study language | English |
| Year of introduction | 2022/2023 | Year of cancellation | |
| Intended for the faculties | FEI | Intended for study types | Follow-up Master |
Subject aims expressed by acquired skills and competences
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.
Teaching methods
Lectures
Tutorials
Project work
Teaching by an expert (lecture or tutorial)
Summary
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.
Compulsory literature:
L. T. Moss, Shaku Atre: Business Intelligence Roadmap: The Complete Project Lifecycle for Decision-Support Applications. 576p, Addison-Wesley Professional, 2003.
Recommended literature:
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.
Way of continuous check of knowledge in the course of semester
Student will work on tasks related to the topics presented on lectures. The tasks are scored, the credit is passed in the case of more than a half number of points.
E-learning
Other requirements
Basic knowledge of database systems on the level of bachelor study and a basic course of master study.
Prerequisities
Subject has no prerequisities.
Co-requisities
Subject has no co-requisities.
Subject syllabus:
Lectures:
1. Introduction to BI, fundamental of BI, basic architectures and components.
2. Data warehouses. according to Inmona and Kimballa, design patterns.
3. ETL Framework, functional requirements for ETL, architectures.
4. Data govermance, master data management.
5. Data Vault, design, usage.
6. Architecture of modern data warehouse.
7. Components of Microsoft Azure and Amazon WS for data warehouses.
8. Distribution and vizualization of data in data warehouses.
9. Analytic oved data warehouses, design patterns.
10. BI modeling.
11. OLAP and MDX.
12. Introduction to DAX.
13. BI use cases, practical projects, pros and cons.
14. Management of BI projects.
Practices:
1. SSIS, introduction.
2. SSIS, data loading, basic operations in data flow.
3. SSIS, Delta management.
4. SSIS, Surrogate keys, key mapping, incremental load.
5. SSIS, ETL Framework.
6. SSIS, optimization, performance management.
7. Microsoft Azure - an infrastructure for a data-warehouse.
8. Microsoft Azure - tools for ETL.
9. Microsoft Azure - streaming data.
10. Reporting Services - introduction, implementation.
11. Power BI - model.
12. Power BI - data visualization.
13. Power BI - project management.
14. Microsoft Azure - AI, machine learning.
Conditions for subject completion
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction
Předmět neobsahuje žádné hodnocení.