In today's world, where data plays a crucial role, effective organization of information is the foundation for quality analytics and report building. Multidimensional data modeling is an essential approach that structures data for quick access and informed decision-making.
Course Overview
This course provides a comprehensive introduction to the fundamental concepts of dimensional modeling. You will learn the mechanics of fact and dimension tables, understand what slowly changing dimensions (SCD) are, and explore the different types of fact tables.
Throughout the course, you will gain practical experience in setting up and working with a data warehouse using real-world tools like DuckDB and DBeaver. By the end of the training, you will be well-equipped to design a data model for high-performance analytics and reporting.
What You Will Learn
Introduction to Data Warehouses
Learn the fundamentals of building data warehouses and understand their importance in analytical processing. Discover how a data warehouse consolidates information from various sources to enable fast and scalable analysis.
Basics of Dimensional Modeling
Get acquainted with key elements such as dimensional and fact tables. Learn how to design a data structure for analytics, determine business metrics, identify suitable dimensions, and effectively link them together. Through practical examples, master the principles of building a meaningful and efficient data model.
Setting Up a Data Warehouse
Gain hands-on experience with DuckDB and DBeaver: create and manage tables, and prepare the environment for analytics. This module includes step-by-step instructions for setting up the environment and deploying your first data warehouse.
Working with a Data Warehouse
Delve into advanced topics such as managing slowly changing dimensions (SCD) and working with different types of fact tables, including transactional and cumulative. Understand how to track business events and trends, optimize queries, and analyze large volumes of data.
