Delivered at:: Department of Business Informatics

Course type:: Compulsory course

When:: 3 year, 1 module

Instructors

Aleshkin, Ramil

Gomenyuk, Kirill

Neklyudov, Dmitry

Full Syllabus

Abstract

Managers need to be properly informed in order to take appropriate decisions to keep up with business successfully. Data possessed by organizations are usually scattered among different systems, each one devised for a particular kind of business activity. Further, these systems may also be distributed geographically in different branches of the organization. Data warehousing is nowadays a technology widely used by organizations to improve their operations and better achieve their objectives. Data warehouse accumulates information from different sources into a single repository suited for analysis. Development of analytical databases requires special approach that involves architectures, tools, and techniques for bringing together data from heterogeneous information sources. In this course students will learn to create architected and virtual data marts, design data flow and build business intelligence reports.

Learning Objectives

The course familiarizes students with basic principles of building a data warehouse and focuses on data mart design, loading data and creating reports on top of star schema data source. Practice includes ETL procedures development, building a virtual data mart in SAP HANA platform and creating business intelligence reports in Tableau.
Provide students with necessary knowledge and practical skills in business database design and maintenance
Learn to use SQL programming language to read and process the data stored in a relational database

Expected Learning Outcomes

Build a logical data model on top of a conceptual model
Create database objects with SQL - procedures, functions and views
Describe a relational database schema
Develop complex queries with use of window functions
Explore dependencies of attributes and solve data redundancy problems
Find a plan for retrieving data from a relational DB in terms of relational operations
Identify data model types
Identify entities and relationships based on business rules analysis and create conceptual and logical models
Organize data in table form
Prepare reports and dashboards to provide access for users to business-critical information
Retrieve data from connected tables
Track and process changes in table data with triggers
Use calculations in SQL queries
Use client software to manage and fill a relational database
Use IDEF1X method to create a database
Use query editor to write and execute queries
Use spreadsheet tools to store and process data
Write analytical queries with aggregation
Know basic components of data warehouse
Know difference between architected and virtual data marts
Know the difference between OLTP and OLAP systems
Learn to build business intelligence reports on top of modern data processing platforms (SAP HANA 2)
Students will be able to organize data flow from sources to staging area and data marts
Understand dimensional modelling principles
Understand functions of ETL tools (Talend)
Understand main approaches to building a data warehouse

Course Contents

Introduction to data management. Database systems
Introduction to data warehouse design
Relational algebra. Retrieving data from multiple tables in DQL
Data warehouse architecture
Database design
ETL subsystem of data warehouse
Data manipulations in structured query language. Create, read, update and delete operations.
Virtual data mart and business intelligence systems
Advanced SQL. Analytical queries. Procedural SQL
Reporting and data visualization

Assessment Elements

Team project
Test
Exam
Practice
Quiz on lecture
Test
Assignments on lectures
Small assignments in form of tests will be given to the students at the end of each lecture.
In class practice
Team project
The team size is 2-3 students. The results should be published on the shared database server. Reports should be uploaded on a cloud disk service.

Interim Assessment

2020/2021 4th module
0.25 * Team project + 0.2 * Exam + 0.2 * Test + 0.1 * Quiz on lecture + 0.25 * Practice
2021/2022 1st module
0.15 * In class practice + 0.15 * Assignments on lectures + 0.3 * Test + 0.4 * Team project

Bibliography

Recommended Core Bibliography

Garcia-Molina, H., Ullman, J. D., Dawson Books, & Widom, J. (2014). Database Systems: Pearson New International Edition : The Complete Book (Vol. Second edition). Harlow, Essex: Pearson. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1418178
Hoffer, J. A., Ramesh, V., & Topi, H. (2016). Modern Database Management, Global Edition (Vol. Global edition, Twelfth edition). Boston: Pearson. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1419666
Kimball, Ralph, and Margy Ross. The data warehouse toolkit: The definitive guide to dimensional modeling. John Wiley & Sons, 2013.
Paulraj Ponniah. (2010). Data Warehousing Fundamentals for IT Professionals: Vol. 2nd ed. Wiley.

Recommended Additional Bibliography

Clark, D. (2017). Beginning Power BI : A Practical Guide to Self-Service Data Analytics with Excel 2016 and Power BI Desktop (Vol. Second edition). Camp Hill, Pennsylvania: Apress. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1478775
Foster, E. C., & Godbole, S. (2016). Database Systems : A Pragmatic Approach (Vol. Second edition). [United States]: Apress. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1174505
Inmon, W. H., Linst, D., & Levins, M. (2019). Data Architecture: A Primer for the Data Scientist : A Primer for the Data Scientist (Vol. Second Edition). London: Academic Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1951596

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