Objectives and outcomes
Acquiring general knowledge of data warehouse design and implementation, as well as important
business intelligence concepts. Upon completion of the course, students will have a basic knowledge of
how data warehouses function, as well as of other important concepts of business intelligence.
Students know how to implement the ETL process and simpler data marts by applying the available
tools. Students know how to use several analytical tools of the end user. Also, they have a basic
knowledge of BSC strategic management methodology and its application together with data warehouse
The place and role of a data warehouse (DW) in the information system of a modern enterprise, basic
concepts and components (data sources, data staging, metadata, data warehouses, data marts,
management and control, information delivery). Kimball and Inmon approach to building a data
warehouse. Data warehouse planning and management. Approaches to data warehouse design (top-
down, bottom-up, practical approach). Data warehouse architecture and infrastructure. Dimensional
modelling (fact tables, data granularity, types of fact tables; dimension tables, principles of change of
values in fact tables and dimension tables, slowly changing dimensions, types of dimension tables).
Data warehouse schemes (Star, Snowflake, Fact Constellation). ETL (Extraction, Transformation,
Loading) process and tools, integration with ERP and other transactional systems. OLAP (data cubes,
operations: roll-up / drill-down, slice / dice, pivot; OLAP models; FASMI test for OLAP, OLAP tools). KPI
(Key Performance Indicators) and the concept of a dashboard. Elements of BSC (Balanced Scorecard)
methodology of strategic management and their implementation using DW.
Data analysis in a specific operational data store. Business process selection, granularity selection,
dimension identification and numerical fact identification in modelling a star data warehouse schema.
Implementation of different ways of data extraction from operational systems. Application of data
transformation methods using query language and available tools. Initial loading of data into the data
warehouse. Incremental loading of data into the data warehouse using different techniques. Creating a
multidimensional model for data analysis. Application of roll-up / drill-down, slice / dice, pivot operations
in data analysis. Using KPIs to determine business metrics.