: Kimball insists on storing data at the lowest level of detail (the "grain") to ensure maximum flexibility for future analysis. 🛠️ Key Techniques Introduced
: Uses "Conformed Dimensions" (standardized lists like a master customer list) so different data marts can "talk" to each other.
: 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.
