Introduction to Kimball Methodology
Kimball Methodology is nothing but a bunch of pre-defined processes and practices used for developing, designing & maintaining a data warehouse by applying the bottom-up approach for these processing. The most recognized benefits of using the Kimball methodology are the database design, development & build can be completed faster compared to other methods, cost for implementation & maintenance is lower, takes lesser time to develop as it is a pre-planned practice, etc.
What is Kimball Methodology?
Kimball is a set of defined methods, processes and techniques that are used to design and develop a data warehouse It is also referred with different names such as bottom-up approach, Kimball’s dimensional modeling and data warehouse life cycle model by Kimball.
Before we proceed further let us understand a few terms:
Data Mart: It is a specific repository of data that was designed to answer specific questions. Multiple data marts exist in different field areas.
Data Warehouse: It is an enterprise-wide repository that contains the information of the entire organization.
Kimball follows a different approach in which dimensional data marts are created first. The data sources are then identified and fetched from various sources and loaded. The star schema is the indispensable factor of dimensional modeling. Multiple such schemas can exist in a single model. To organize data in an integrated manner, Kimball recommends that the dimension table must be shared with different tables within various data marts. This helps us to comprehend that a single piece of data can be used in a similar manner throughout all the facts.
How does the Kimball Methodology Work?
The focus of the Kimball approach is on identifying the key business process and the subsequent business solutions that we need to provide with the data warehouse. The Kimball approach utilizes dimensional models such as star and snowflake schema to organize the data into various business classified data, in order to quickly enable business processes. Now from an architectural perspective, Kimball proposes that it isn’t necessary to separate the data marts from the existing dimensional data warehouse.
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Finally, Kimball focuses on providing analytical systems that can be directly accessed from the data warehouse. In other words, Kimball proposes an architecture where an analytical system can directly consume data from the data warehouse. It takes relatively less time to implement Kimball data warehouse architecture. Since the abstraction is a higher level of abstraction, we only consider the data warehouse design using a star or snowflake schema.
While in the case of Kimball based design, maintenance is difficult because there can be redundant data and revisions require additional tasks. Kimball incurs low initial cost because we only need to plan the data warehouse and the cost remains the same for the subsequent phases. It requires a general team to implement. The resources involved need to know how to work with ER modeling, without the need to decouple them into various data marts. Also with Kimball based data warehouse, the data integration requirement is focused on the individual business area. Kimball-based design, maintenance is difficult because there can be redundant data.
Advantages of Kimball Methodology
- It takes a relatively lesser amount of time to implement the Kimball data warehouse architecture since the abstraction is at a higher level.
- Kimball incurs low initial cost because we only need to plan the data warehouse and the cost remains the same for the subsequent phases.
- Kimball-based data warehouses can be set up quickly.
- Kimball requires a generalist team to implement.
- In Kimball methodology, the data integration requirement is focused on the individual business area.
Conclusion
Kimball architecture requires data sources, data staging, ETL capabilities, and data marts. Business requirements need to be captured and they both require time attribute for data to facilitate historical data. Kimball happens to be process-oriented since the focus is on business processes. Kimball prefers the denormalized data model, and as such, we find redundant data model present in the Kimball architecture. Kimball based data warehouses are easier to design and implement. We have to adapt to the changes and the data warehouse level. With Kimball, we get one data warehouse storage that we need to utilize logically depending on the business process.
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