Dimensional modeling contributes to being the data structure process which is optimized for data warehousing tools. Ralph Kimball came up with the concept of dimensional modeling.
The dimensional modeling primarily contains dimensional table and the fact table. A dimensional model is beneficial for summarizing, reading and analysis of numeric details such as balances, values, weights, and counts in the data warehouse.
Relation models, on the other hand, are optimized for updating, addition as well as removal of data in real time online transaction system.
The relational and dimensional models boast of their unique ways for the storage of data, which has its certain benefits. For example, in the relational model, the ER and normalization models are effective in bringing a reduction in the data redundancy. Dimensional modeling, on the other hand, arranges data in a specific manner and thus it helps in the easy retrieval of information and generation of reports.
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Steps in dimensional modeling
The accuracy to develop dimensional modeling determines the success of the implementation of data warehouse solutions.
Few steps that are involved in the creation of dimensional modeling involves the identification of the business process, identification of facts, identification of dimensions, gains, as well as building the start. The dimensional modeling should be capable of defining the different aspects of the business process.
Identification of different business process
The first step involves the identification of the actual process of business, which should be covered by the data warehouse. It can be sales, marketing, and HR, catering to the data analysis requirements of the business.
Choosing a specific business procedure depends on the quality of data, which is available for the certain procedure. It is regarded as an integral part of the data modeling procedure. A failure, in the same, may lead to irreparable and cascading defect. For describing the process, you can make use of BPMN or Business Process Modeling Notation or UML, referred to as Unified Modeling Language.
Identification of the grain
The grain is known to describe the level of detail for the specific solution or problem of the business. In this process, the least level of information for various tables, present in the data warehouse is identified.
In case the table comprises of sales data daily, it should be daily granularity. However, if the table consists of the total sales data for the entire month, it comes with the monthly granularity. In this specific phase, you require giving answers for different questions like whether you require storing every available product or a few kinds of products. Such decision is based on different processes of the business, chosen from the data warehouse.
Identifications of dimensions
Dimensions refer to nouns such as store, date, inventory, to name a few. All the data are known to be stored in these dimensions. For instance, the data dimension might comprise of data such as a month, year of the weekday.
Identification of Fact
Such a phase is associated with the users of the business of the system as it is the place to get access to the data, which is stored in the data warehouse. Also, majority of the fact table rows are recognized to be numerical values such as cost or price per unit.
Building the schema
The implementation of Dimensional modeling is achieving in this specific phase. A schema refers to the structure of database. There are primarily two kinds of schemas which are referred to as the Star Schema and Snowflake Schema.
It is easy to design the architecture of the star schema. It is referred to as the star schema as the diagram showcases the resemblance of the star, along with points, which radiate from the center. The center of the star consists of the fact table. In addition to this, the points of the star are recognized to be dimensional tables.
It is considered to be the extension of the star schema. In this snowflake schema, each dimension, after being connected to different dimension tables is normalized.
Why choose dimensional modeling
The standardization of the dimension ensures hassle free reporting across various areas of the business. Dimension tables are useful to store the history of dimensional information.
It plays a vital role in introducing new dimension without any disruption in the fact table. You also need to keep in mind that dimensional modeling helps in the storage of data and thus it helps in extracting details from the data, which is stored in the database.
It is easy to understand the dimensional table, in comparison to the normalized model. It is possible to group the information into simple and clear business categories in dimensional modeling.
Business firms can understand the dimensional modeling easily. It is known to be based on different business terms.
Hence, the business has an understanding of the meaning of every attribute, dimension and fact. Furthermore, dimensional models are known to be deformalized and optimized to ensure quick querying of data. There is a wide assortment of relational database platform that recognizes the model after which it should be optimizing the different plans of query execution for boosting the performance.
Dimensional modeling plays a vital role in providing a boost to the query performance. It is known to be more denormalized, and thus it is optimized for the querying purpose.
Dimensional modeling is capable of accommodating the changes comfortably. The dimension tables are known to have more columns, which are added to them without having an effect on the existing applications of business intelligence with the aid of the tables.The dimension model contributes to being the data structure technique which is optimized for a wide assortment of data warehousing tools.
Dimensions offer the content which is known to surround the event of business process. Dimensional modeling involves the loading of atomic data into the dimensional structures. The dimensional models are built around different processes of the business. It is recommended to ensure the storage of different report labels, and filtering the various domain values in the dimension tables. You should ensure that the dimensional tables make use of the surrogate key.