Inmon’s enterprise data warehouse approach, a normalised data model is designed first, then the dimensional data marts, which contain data required for specific business processes or specific departments, are created from the data warehouse. In Kimball’s dimensional design approach, the data marts facilitating reports and analysis are created first; these are then combined together to create a broad data warehouse.
In Kimball’s dimensional design approach, the data marts facilitating reports and analysis are created first; these are then combined together to create a broad data warehouse.
What is the Approach of Kimball? ?
Kimball approach of designing a Dataware house was introduced by Ralph Kimball.
This approach starts with recognizing business process and questions that Dataware house has to answer. These sets of information are being analyzed and then documented well.
The Extract Transform Load (ETL) software brings all data from multiple data sources called data marts and then is loaded into a common area called staging. Then this is transformed into OLAP cube.
What is the Approach of Inmon?
Inmon approach of designing a Dataware house was introduced by Bill Inmon. In Inmon’s philosophy, it is starting with building a big centralized enterprise data warehouse where all available data from transaction systems are consolidated into a subject-oriented, integrated, time-variant, and non-volatile collection of data that supports decision making. Dimensional data marts are created only after the complete data warehouse has been created. Thus, the data warehouse is at the centre of the corporate information factory, which provides a logical framework for delivering business intelligence.
Comparison of Kimball and Inmon
• While Kimball utilizes dimensional models for the data, Inmon utilizes dimensional models just for the data marts.
• With the Kimball approach, the focus is on identifying the key business process and the subsequent business solutions that we need to provide with the data warehouse. While the Inmon approach believes in building a data warehouse with the corporate data model.
• The Kimball approach utilizes dimensional models such as star and snowflake schema to organize the data into various business classified data to enable business processes quickly. Inmon, on the other hand, considers the overall corporate data requirement, and as such, it utilizes the entity relationship modelling technique.
• Kimball proposes an architecture where an analytical system can directly consume data from the data warehouse. While in the case of Inmon, the architecture is designed so that the analytical system can only access the data from the data warehouse through the data marts.
• When we are implementing Inmon based data warehouse, we’ll need a specialist team since we need to design data marts as well. To design data marts, the team has to be familiar with each functional unit. Kimball, on the other hand, requires a general team to implement.
• In the Kimball approach source systems are highly stable. On the other hand, in the Inmon approach source systems have high rate of change.
• Implementing an Inmon-based data warehouse can incur a high initial cost, but the subsequent project development costs are lower because it requires less maintenance and load change adaptive. On the other hand, Kimball incurs a low initial cost because we only need to plan the data warehouse, and the cost remains the same for the subsequent phases. Inmon based architecture of data warehouse requires longer startup time, whereas Kimball based data warehouses can be set up quickly.