Dimensional Data Modeling
tags:
- data-engineering
- data-modeling
- data-dimensional
Introduction to Dimensional Modeling
Understanding the basics of Dimensional Modeling Exploring the concepts of Facts and Dimensions Introduction to Star Schema and Snowflake Schema Advantages of Dimensional Modeling in data analysis and reporting
Create a simple entity relationship ERD for sample business model (sale operation) Create a simple dimensional model for a sample dataset (e.g., sales transactions) Identify the fact tables and dimension tables Design a star schema based on the identified fact and dimension tables Use drawing tools or whiteboards to visualize the dimensional model
Star Schema Design
Deep dive into Star Schema design principles Understanding the structure of a star schema: Fact table and Dimension tables Different types of dimensions: Conformed dimensions, Junk dimensions, Degenerate dimensions Best practices for designing efficient star schemas
Refine the dimensional model created on Object 1 by implementing a star schema design Normalize dimension tables to remove redundancy and improve data integrity Populate dimension tables with sample data Verify the relationships between the fact table and dimension tables
Snowflake Schema Design
Introduction to Snowflake Schema and its differences from Star Schema Understanding the benefits and use cases of Snowflake Schema Normalization techniques in Snowflake Schema design When to choose Snowflake Schema over Star Schema
Modify the existing dimensional model to implement a Snowflake Schema design Normalize dimension tables further to create a snowflake structure Populate the snowflake schema with sample data Compare the performance and scalability of the Snowflake Schema with the Star Schema
Advanced Dimensional Modeling Techniques
Exploring advanced dimensional modeling techniques Introduction to slowly changing dimensions (SCDs) Handling hierarchical data in dimensional models Hybrid modeling approaches: Bridge tables, Role-playing dimensions
Extend the existing dimensional model to include slowly changing dimensions (Type 1 and Type 2) Implement bridge tables to handle many-to-many relationships or recursive hierarchies Create role-playing dimensions to represent different perspectives of the data Populate the modified dimensional model with sample data and test the SCD handling
Dimensional Modeling Best Practices and Review
Review of best practices and common pitfalls in dimensional modeling Guidelines for maintaining and evolving dimensional models over time Integration of dimensional modeling with ETL processes and data warehouses Real-world case studies and success stories
Conduct a review of the dimensional models created during the bootcamp Discuss the challenges faced and lessons learned during the modeling process Brainstorm ideas for applying dimensional modeling principles to real-world scenarios Prepare a presentation showcasing the dimensional models and insights gained