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Building Better Data Warehouses with Dimensional Modeling: A Guide for Data Engineers
Data Warehouse Dimensional Modeling Design 101
Regarding system design for a data-intensive application, it usually comes with two options: write or read optimized.
There isn’t a database design that fits and optimizes both writing and reading. Like all system design perspectives, no solution is right or wrong, while only pros and cons. As data professionals who work on data model design, a critical part of the role is to identify the use case and further identify which design principle should be applied.
The data warehouse has historically acted as the layer serving data to the end users, and it’s the last mile to convert data to insights. Ralph Kimball developed one of the famous modeling design techniques called dimensional modeling. His The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling, 3rd Edition, is the most critical book for dimensional modeling.
Although big data and cloud computing technologies unblock us from using more computing power and cheaper storage, new or even experienced data engineers have overseen the data warehouse modeling design. Fewer people pay attention to types of slow change dimension (SCD), surrogated key, table granularity, etc. concepts…
