Kimball Method

The Kimball Method is a dimensional modeling approach for building a data warehouse that builds the data warehouse from business‑focused data marts, using fact and dimension tables to create a fast, intuitive, and scalable analytical environment.  The Kimball method is based on a series of key principles that define how data should be structured and organized to facilitate its analysis and exploitation. These principles form the basis of dimensional modeling, providing a clear and systematic framework.

The Kimball Method was conceived during the 1980s by Ralph Kimball and other colleagues at Metaphor Computer Systems. Since then, it has been successfully utilized by thousands of data warehouse and business intelligence project teams.

Kimball and his colleagues did not invent the idea of multidimensional data or even the earliest forms of star schemas; what they did was formalize, popularize, and systematize them into the modern dimensional modeling method used worldwide. Kimball’s contribution was codification; turning scattered ideas into a rigorous, teachable, repeatable methodology. This resulted in industry norms for data warehouses and business intelligence.

Fact tables are the foundation of a data warehouse. Dimensional modeling is a logical design technique that data in a standard, intuitive framework that converts the facts in a fact table into usable information.

What is happening now is that a third layer is being added to the previous two layers people have been working with for the past 40 or 50 years.

  • Operational Layer (OLTP): The operational layer is the layer of data and transactions. The operational layer feeds the analytics layer. Entity Relationship Modeling (ER) is used to define the structure of this operational layer.
  • Analytics Layer (OLAP): The analytics layer repackages operational data into fact tables, dimension tables, star schemas, snowflake schemas, conformed dimensions, measures, levels; Dimensional Modeling (DM) is used to describe this layer.  The analytics layer feeds the semantic layer.
  • Semantic Layer (Semantic Layer): The meaning layer, also called the semantic layer, whereas the operational layer captures and stores the data and the analytics layer organizes the data for  reporting and analysis; the meaning or semantics layer explains what that data means and provides understanding, and trusted understanding referred to as knowledge. A semantic layer represents your business information clearly in terms understandable to both humans and artificial intelligence. It provides a single point of reference or “single source of truth“ from which all analytics tools can draw their information.

It seems like the notions of a "semantic layer" and the "enterprise knowledge graph" are at odds. Maybe not.  Some say the semantic layer and the enterprise knowledge graph are not competing approaches; they solve different problems and work best when used together: the enterprise knowledge graph defines meaning, and the semantic layer defines analytic behavior.  I don't quite understand that that means yet.

But, my Seattle Method seems to provide something similar to what the Kimball Method provided: codification; turning scattered ideas into a rigorous, teachable, repeatable methodology.  Core to my method is the information block or information Lego.

The image below comes from the article, What is a Semantic Layer? (Components and Enterprise Applications), which is quite useful if you want to understand what a semantic layer is.


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