Fact Data Modeling
tags:
- data-engineering
- data-modeling
- fact-data
Introduction to Fact Modeling
Understanding the fundamentals of Fact Modeling Exploring the concept of Facts and Fact Tables Importance of Fact Modeling in data analysis and reporting Different types of facts: Additive, Semi-additive, and Non-additive facts
Identify a sample dataset suitable for fact modeling (e.g., sales transactions, customer interactions) Define the key performance indicators (KPIs) that need to be captured in the fact table Design a basic fact table structure based on the identified KPIs Use drawing tools or whiteboards to visualize the fact model
Additive and Semi-additive Facts
Deep dive into Additive and Semi-additive facts Understanding the characteristics and usage scenarios of each type Examples of commonly encountered Additive and Semi-additive facts in different domains Best practices for modeling and aggregating Additive and Semi-additive facts
Refine the fact table designed on Object 1 to accommodate both Additive and Semi-additive facts Populate the fact table with sample data representing various transactions or events Perform basic aggregation queries to understand how Additive and Semi-additive facts are aggregated
Non-additive Facts and Advanced Fact Modeling Techniques
Introduction to Non-additive facts and their significance in data analysis Challenges and considerations when dealing with Non-additive facts Advanced Fact Modeling techniques: Derived facts, Factless facts, Fact constellation Use cases and real-world examples showcasing the application of advanced Fact Modeling techniques
Extend the existing fact model to include Non-additive facts and derived facts Design and implement a factless fact table to capture relationships between dimensions without numerical measures Create a fact constellation to represent complex interrelationships between multiple facts Populate the modified fact model with sample data and perform analysis using SQL queries
Fact Modeling in Multi-dimensional Analysis
Integration of Fact Modeling with multi-dimensional analysis techniques Introduction to OLAP (Online Analytical Processing) and its role in multi-dimensional analysis Building OLAP cubes using Fact Modeling principles Leveraging MDX (Multidimensional Expressions) for querying OLAP cubes
Use a BI tool or OLAP engine to create a multi-dimensional model based on the existing fact model Define dimensions and hierarchies within the OLAP cube Load data from the populated fact model into the OLAP cube Write MDX queries to retrieve aggregated data from the OLAP cube and analyze trends and patterns
Fact Modeling Best Practices and Review
Review of best practices and common pitfalls in Fact Modeling Guidelines for maintaining and evolving Fact Models over time Integration of Fact Modeling with ETL processes and data warehouses Real-world case studies and success stories
Conduct a review of the fact models created during the bootcamp Discuss the challenges faced and lessons learned during the modeling process Brainstorm ideas for applying Fact Modeling principles to real-world scenarios Prepare a presentation showcasing the fact models and insights gained