Dimensional Modeling (DM) is a data structure technique optimized for data storage in a Data warehouse. The purpose of dimensional modeling is to optimize the database for faster retrieval of data. The concept of Dimensional Modelling was developed by Ralph Kimball and consists of “fact” and “dimension” tables. Show A dimensional model in data warehouse is designed to read, summarize, analyze numeric information like values, balances, counts, weights, etc. in a data warehouse. In contrast, relation models are optimized for addition, updating and deletion of data in a real-time Online Transaction System. These dimensional and relational models have their unique way of data storage that has specific advantages. For instance, in the relational mode, normalization and ER models reduce redundancy in data. On the contrary, dimensional model in data warehouse arranges data in such a way that it is easier to retrieve information and generate reports. Hence, Dimensional models are used in data warehouse systems and not a good fit for relational systems. In this tutorial, you will learn- Elements of Dimensional Data ModelFactFacts are the measurements/metrics or facts from your business process. For a Sales business process, a measurement would be quarterly sales number DimensionDimension provides the context surrounding a business process event. In simple terms, they give who, what, where of a fact. In the Sales business process, for the fact quarterly sales number, dimensions would be
In other words, a dimension is a window to view information in the facts. AttributesThe Attributes are the various characteristics of the dimension in dimensional data modeling. In the Location dimension, the attributes can be
Attributes are used to search, filter, or classify facts. Dimension Tables contain Attributes Fact TableA fact table is a primary table in dimension modelling. A Fact Table contains
Dimension Table
Following are the Types of Dimensions in Data Warehouse:
Steps of Dimensional ModellingThe accuracy in creating your Dimensional modeling determines the success of your data warehouse implementation. Here are the steps to create Dimension Model
The model should describe the Why, How much, When/Where/Who and What of your business process Step 1) Identify the Business ProcessIdentifying the actual business process a datarehouse should cover. This could be Marketing, Sales, HR, etc. as per the data analysis needs of the organization. The selection of the Business process also depends on the quality of data available for that process. It is the most important step of the Data Modelling process, and a failure here would have cascading and irreparable defects. To describe the business process, you can use plain text or use basic Business Process Modelling Notation (BPMN) or Unified Modelling Language (UML). Step 2) Identify the GrainThe Grain describes the level of detail for the business problem/solution. It is the process of identifying the lowest level of information for any table in your data warehouse. If a table contains sales data for every day, then it should be daily granularity. If a table contains total sales data for each month, then it has monthly granularity. During this stage, you answer questions like
Example of Grain: The CEO at an MNC wants to find the sales for specific products in different locations on a daily basis. So, the grain is “product sale information by location by the day.” Step 3) Identify the DimensionsDimensions are nouns like date, store, inventory, etc. These dimensions are where all the data should be stored. For example, the date dimension may contain data like a year, month and weekday. Example of Dimensions: The CEO at an MNC wants to find the sales for specific products in different locations on a daily basis. Dimensions: Product, Location and Time Attributes: For Product: Product key (Foreign Key), Name, Type, Specifications Hierarchies: For Location: Country, State, City, Street Address, Name Step 4) Identify the FactThis step is co-associated with the business users of the system because this is where they get access to data stored in the data warehouse. Most of the fact table rows are numerical values like price or cost per unit, etc. Example of Facts: The CEO at an MNC wants to find the sales for specific products in different locations on a daily basis. The fact here is Sum of Sales by product by location by time. Step 5) Build SchemaIn this step, you implement the Dimension Model. A schema is nothing but the database structure (arrangement of tables). There are two popular schemas The star schema architecture is easy to design. It is called a star schema because diagram resembles a star, with points radiating from a center. The center of the star consists of the fact table, and the points of the star is dimension tables. The fact tables in a star schema which is third normal form whereas dimensional tables are de-normalized. The snowflake schema is an extension of the star schema. In a snowflake schema, each dimension are normalized and connected to more dimension tables. Also Check:- Star and Snowflake Schema in Data Warehouse with Model Examples Rules for Dimensional ModellingFollowing are the rules and principles of Dimensional Modeling:
Benefits of Dimensional Modeling
Multidimensional data model in data warehouse is a model which represents data in the form of data cubes. It allows to model and view the data in multiple dimensions and it is defined by dimensions and facts. Multidimensional data model is generally categorized around a central theme and represented by a fact table. Summary:
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