Data Modeling for Data Warehousing

Overview of core data modeling concepts for warehousing Differences between transactional databases and data warehouses

Introduction to ER models for foundational data design Dimensional modeling, covering Star and Snowflake schemas

Creating dimension and fact tables with measures, keys, and attributes Types of Slowly Changing Dimensions (SCD) - Types 1, 2, and 3

Understanding normalization in data warehousing for storage optimization Techniques for denormalization and trade-offs involved

Review of best practices for modeling in data warehousing Real-world examples of applying concepts and addressing common challenges

This module introduces cloud-based features in Excel for seamless sharing and collaboration. Lessons Covered: Using Excel in the cloud Students will be equipped to use cloud services, enabling collaborative work and easy access to data remotely.

In this Data Modeling for Data Warehousing course, you will gain the skills to: Understand Data Modeling Foundations: Learn core principles of data modeling specific to data warehouses, such as understanding the distinctions between data warehouses and transactional databases. Design Data Models Using ER and Dimensional Schemas: Explore and implement Entity-Relationship (ER) models and dimensional modeling structures, focusing on Star and Snowflake schemas to meet specific data warehousing needs. Structure Dimensions and Facts: Learn to create fact and dimension tables, including measures, keys, and attributes essential for data organization and analysis. Additionally, understand Slowly Changing Dimensions (SCD) for tracking historical data changes. Optimize Data Storage: Discover normalization techniques to reduce redundancy and save storage, as well as the benefits of denormalization for optimizing data retrieval performance. Apply Best Practices in Data Modeling: Gain insights into real-world best practices for building robust, scalable data models and overcoming common data modeling challenges in warehousing environments. By the end of the course, you'll be able to design effective data models to support accurate reporting, efficient data retrieval, and scalable storage solutions tailored for business intelligence.

Data Warehouse Architects who want to expand their data modeling expertise, particularly in optimizing storage and retrieval. Data Engineers working on data integration and warehouse design, focusing on understanding schema structures. BI Analysts looking to better understand backend modeling for analytics and report accuracy. Database Administrators transitioning into data warehousing roles who need foundational modeling knowledge for warehousing