Description

Theory
1

Theory/Practice
1

Laboratory
3

Instructors

Paulo Oliveira

Contents

1 Introduction to data warehouses (8%)
- Concept
- Main characteristics
- Concept of Data Mart
- General view of dimensional model
- Kinds of schema

2 Dimensional Data Modelling (23%)
- Star schema model
- Dimension tables
- Fact tables
- Kind of facts
- Slowly changing dimensions
- Modelling techniques

3 Data Warehouse Architectures (15%)
- Corporate information factory
- BUS architecture
- Comparison between architectures

4 Extraction, Transformation, Cleaning and Loading Process (23%)
- Data extraction
- Transformation, cleaning and integration
- Data loading

5 Data warehouse optimization (8%)
- Creating indexes
- Creating partitions
- Creating Aggregates

6 On-Line Analytical Processing (8%)
- Advantages
- Kinds of analytical databases
- Basic operations

7 Advanced/Research topics in data warehouses (15%)
- Real-time data warehouses
- Streaming de dados
- Data lake
- Data lakehouse

Learning Outcomes

The course aims to give the students the skills to plan, implement, manage and explore a project of a data warehouse system within an organization.

This course contributes with additional knowledge and competences of:
1. Exploring and maintaining data in environments characterized by high volumes, heterogeneous and from several sources.
2. Modelling and efficient representation of the data to be used in queries dealing with high data volumes.
3. Integration of data coming from heterogeneous sources.

At the end of this course, the student should be able to:
CO1. Describe the terminology and the concepts used in the area (Bloom Level 2).
CO2. Explain a data warehouse architecture and, specifically, the purpose of its components (BL: 4).
CO3. Apply methods in a justified manner that support the analysis, modelling, design and implementation of data warehouses (BL: 5).
CO4. Critically consider alternative scenarios for modeling, designing and implementing a data warehouse (BL: 5).
CO5. Combine techniques that support the extraction, transformation, cleaning, integration, and load processes in a data warehouse (BL: 6).
CO6. Combine techniques that optimize the operation of a data warehouse (BL: 6).
CO7. Create data cubes by applying different techniques to support multidimensional data analysis (BL: 6).