: Methods to track history when attributes change (e.g., when a customer moves to a new city). Type 1 : Overwrite the old data. Type 2 : Create a new row to preserve history (most common). Type 3 : Add a "previous value" column.
Even in the age of , Cloud Warehousing (Snowflake/BigQuery) , and dbt , Kimball’s principles remain the standard. Modern "Data Mesh" or "Lakehouse" architectures still rely on Star Schemas to provide a clean layer for BI tools like Tableau and Power BI.
: These store the descriptive context (attributes) surrounding the facts (e.g., product name, date, store location). 🌟 The "Kimball Method" Principles Kimball & Ross - The Data Warehouse Toolkit 2nd...
: Uses "Conformed Dimensions" (standardized lists like a master customer list) so different data marts can "talk" to each other.
The 2nd edition provides a toolkit of specific patterns for common data problems: : Methods to track history when attributes change (e
: These store the quantitative metrics (measures) of a business process (e.g., sales amount, temperature, duration).
: Build data marts for specific business processes first, then integrate them. Type 3 : Add a "previous value" column
The Data Warehouse Toolkit (2nd Edition) by and Margy Ross is considered the "Bible" of data warehousing. It introduced the Dimensional Modeling methodology, which focuses on making data easy for business users to query and understand. 🏗️ Core Concept: Dimensional Modeling