UNADE · Information Technology
FROM DATA TO INFORMATION. FUNDAMENTALS OF DATA WAREHOUSING
Program Overview
The fundamentals of Data Warehouses are presented as systems designed to support decision-making, emphasizing the integration, organization, and analysis of large volumes of data from diverse sources. Their key characteristics—such as subject orientation, integration, time variance, and non-volatility—are highlighted, along with their role in contrast to traditional transactional systems.
This course presents the main architectural elements and components of Data Warehouses, including multidimensional models, OLAP tools, and the ROLAP and MOLAP approaches. It also highlights the importance of data loading and maintenance processes (ETT) and proper design methodologies, supporting efficient analysis, knowledge generation, and continuous improvement in organizational decision-making
This course is aimed at students and professionals interested in data management and analysis, particularly those seeking to understand how information systems support decision-making processes. It is especially relevant for individuals in fields such as information technology, business intelligence, data analysis, and management.
It is also suitable for professionals involved in the design, implementation, or use of Data Warehouses, including database administrators, data engineers, and business analysts, as well as anyone interested in improving organizational performance through effective use of data.
1. Introduction to Data Warehouses
Understand the fundamental concepts, purpose, and characteristics of Data Warehouses for decision support.
2. Data Warehouse Architecture
Identify the main components and structures, including multidimensional models and data organization.
3. OLAP Tools
Develop the ability to query, analyze, and manipulate data using multidimensional analysis tools.
4. ROLAP and MOLAP
Differentiate between architectures and evaluate their advantages and limitations.
5. Loading and Maintenance of a Data Warehouse
Understand ETL processes and ensure data quality, integration, and updating.
6. Data Warehouse Design
Apply basic methodologies to design efficient Data Warehouse structures adapted to analytical needs.
Study Plan
1. Introduction to Data Warehouses
- Introduction
- Data Analysis for Decision Support
- Data Warehouses (DW)
- Components
2. Data Warehouse Architecture
- Multidimensional Model
- Hierarchies
- Data Mart
3. OLAP Tools
- Data Querying
4. ROLAP and MOLAP
- Introduction
- ROLAP Systems
- MOLAP Systems
- ROLAP/MOLAP: Advantages and Disadvantages
5. Loading and Maintenance of a Data Warehouse
- E.T.T. (Extraction - Transformation - Transport)
- Extraction
- Transformation
- Transport
- Post-load processes
6. Data Warehouse Design
- Introduction
- Analysis
- Design
- Other Design Guidelines