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Dimensional modeling (DM) is the name of a set of techniques and concepts used in data warehouse design. It is considered to be different from entity-relationship modeling (ER). Dimensional Modeling does not necessarily involve a relational database. The same modeling approach, at the logical level, can be used for any physical form, such as multidimensional database or even flat files. According to data warehousing consultant Ralph Kimball, DM is a design technique for databases intended to support end-user queries in a data warehouse. It is oriented around understandability and performance. According to him, although transaction-oriented ER is very useful for the transaction capture, it should be avoided for end-user delivery.
Dimensional modeling always uses the concepts of facts (measures), and dimensions (context). Facts are typically (but not always) numeric values that can be aggregated, and dimensions are groups of hierarchies and descriptors that define the facts. For example, sales amount is a fact; timestamp, product, register#, store#, etc. are elements of dimensions. Dimensional models are built by business process area, e.g. store sales, inventory, claims, etc. Because the different business process areas share some but not all dimensions, efficiency in design, operation, and consistency, is achieved using conformed dimensions, i.e. using one copy of the shared dimension across subject areas. The term "conformed dimensions" was originated by Ralph Kimball.
Business requirements and expectations of the warehouse have been carefully refined by the analysts through frequent facilitation sessions with the users. Analysis of the operational source systems has been performed and a business logical model has been created and agreed upon by the joint development team. A strategy for acquisition and use of a metadata repository is complete with a design in progress. The physical data warehouse data model has gone through several evolving iterations and is nearly finished. The technical development environment has been established and is fully operational. Everything is in place to begin construction of the back-end data warehouse environment is set when a new front-end reporting requirement is identified during a review of data access needs.
The front-end reporting environment of the data warehouse must allow the user to choose either a current (recasting of history) or historical view perspective of dimension table information for all reports. This new business requirement has several consequences on the data model design plus the extraction, transformation and loading (ETL) processes. For example, when a profitability report is run for a three year period on client "Smith & Peters International" who last year, before the merger, was just Smith International and two years before that was just Smith Corporation. If a historical perspective of the report is requested, facts are broken down over the three-year period by each of the three transformations the company has gone through. If a current view perspective is selected, that ignores the previous transformations of the company, all facts are presented for the three periods grouped under Smith & Peters International.
The dimensional model is built on a star-like schema, with dimensions surrounding the fact table. To build the schema, the following design model is used:
The process of dimensional modeling builds on a 4-step design method that helps to ensure the usability of the dimensional model and the use of the data warehouse. The basics in the design build on the actual business process which the data warehouse should cover. Therefore the first step in the model is to describe the business process which the model builds on. This could for instance be a sales situation in a retail store. To describe the business process, one can choose to do this in plain text or use basic Business Process Modeling Notation (BPMN) or other design guides like the Unified Modeling Language (UML).
After describing the Business Process, the next step in the design is to declare the grain of the model. The grain of the model is the exact description of what the dimensional model should be focusing on. This could for instance be “An individual line item on a customer slip from a retail store”. To clarify what the grain means, you should pick the central process and describe it with one sentence. Furthermore the grain (sentence) is what you are going to build your dimensions and fact table from. You might find it necessary to go back to this step to alter the grain due to new information gained on what your model is supposed to be able to deliver.
The third step in the design process is to define the dimensions of the model. The dimensions must be defined within the grain from the second step of the 4-step process. Dimensions are the foundation of the fact table, and is where the data for the fact table is collected. Typically dimensions are nouns like date, store, inventory etc. These dimensions are where all the data is stored. For example, the date dimension could contain data such as year, month and weekday.
After defining the dimensions, the next step in the process is to make keys for the fact table. This step is to identify the numeric facts that will populate each fact table row. This step is closely related to the business users of the system, since this is where they get access to data stored in the data warehouse. Therefore most of the fact table rows are numerical, additive figures such as quantity or cost per unit, etc.
Dimensional normalization or snowflaking removes redundant attributes, which are known in the normal flatten de-normalized dimensions. Dimensions are strictly joined together in sub dimensions.
Snowflaking has an influence on the data structure that differs from many philosophies of data warehouses. Single data (fact) table surrounded by multiple descriptive (dimension) tables
Developers often don't normalize dimensions due to several facts:
There are some arguments on why normalization can be useful. It can be an advantage when part of hierarchy is common to more than one dimension. For example, a geographic dimension may be reusable because both the customer and supplier dimensions use it.
Benefits of the dimensional modeling are following: