His design methodology is called dimensional modeling or the Kimball methodology. This methodology focuses on a bottom-up approach. In terms of how to architect the data warehouse, there are two distinctive schools of thought: the Inmon method and Kimball method. They both. Kimball's approach, on the other hand, is often called bottom-up because it starts and ends with data marts, negating the need for a physical data warehouse.


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Inmon or Kimball: Which approach is suitable for your data warehouse?

Bill Inmon saw a need to integrate data from different OLTP systems into a centralized repository called a data warehouse with a so called top-down approach. Bill Inmon envisions a data warehouse at center of the "Corporate Information Factory" CIFkimball methodology provides a logical kimball methodology for delivering business intelligence BIbusiness analytics and business management capabilities.

This top-down design provides a highly consistent dimensional view of data across data marts as all data marts are loaded from the centralized repository Kimball methodology Warehouse.

The top-down design has also proven to be flexible to support business changes as it looks at the organization as whole, not at each function or business process of the organization.

The normalized source tables for Product attributes The base table is called Product and it connects to the Sales fact table at the individual product key level. In the dimensional version of the Product table, we would join the product-related tables from Figure 4 once, during the ETL process, to produce a single Product dimension table.

Figure 5 shows the resulting Product dimension based on the tables and attributes in Figure 4. Note that the two kimball methodology are equivalent from an analytic perspective.

Usability is significantly improved for BI application developers and ad-hoc users with the dimensional version. In this simple example, the ten tables that contain the 12 product attributes are combined into a single table.

This 10 to 1 reduction in kimball methodology number of tables the user and optimizer must deal with makes a big difference in usability and performance. When you apply this across the 15 or 20 dimensions you might typically find associated with a Sales business process, the benefits are enormous.

The main difference between the two approaches is that the normalized version is easier to build if the source system is already normalized; but the dimensional version is easier to use and will generally perform better for analytic queries.

The Kimball Lifecycle

Tracking Attribute Variations over Time Every analytic data store must provide a kimball methodology to accurately track dimension attributes as they change over time. For example, what postal code kimball methodology a customer live in when they bought a certain product two years ago?


The kimball methodology efficient way to capture these changes from both an ease kimball methodology use and performance perspectives is to add a row to the dimension whenever an attribute changes by assigning a new surrogate key and capturing the effective date and end date for each row.

These are commonly referred to as slowly changing Type 2 dimensions. You can see these control columns at the bottom of the Product dimension in Figure 5.

While tracking attribute changes over time kimball methodology a burden on the ETL process, it improves performance for kimball methodology queries because the joins between the facts and dimensions are simple equijoins on integer keys.

Also, the retrieval of data from the data warehouse tends to operate very quickly. Plus, if you are used to working with a normalized approach, it can take a while to fully understand the dimensional approach and to become efficient in building one. ETL system strives to deliver high throughput, as well as high quality output.


The goal is to deliver capabilities that are accepted by the business kimball methodology support and enhance their decision making. This will give desired specification of the tool required. Later, we configure the business metadata and tool infrastructure.

Inmon or Kimball: Which approach is suitable for your data warehouse?

You have exceeded the maximum character limit. Please provide a Corporate E-mail Address. Please check the box if you want to proceed. As a result, some companies kimball methodology to adopt a clear vision for the way the data warehousing environment can and should evolve.

Others, paralyzed by confusion or fear of deviating from prescribed tenets for success, cling too rigidly to one approach or another, undermining their ability to respond flexibly to new or unexpected situations.

Ideally, organizations need to borrow concepts and tactics from each approach to create environments that uniquely meets their needs. Semantic and Substantive Differences The two most kimball methodology approaches are championed by industry heavyweights Bill Inmon and Ralph Kimball, both prolific authors and consultants in the data warehousing field.