Popular Schema – Star Schema, Snow Flake Schema
Dimensional Data Modeling is one of the data modeling techniques used in data warehouse design.
Goal: Improve the data retrieval.
The concept of Dimensional Modeling was developed by Ralph Kimball which is comprised of facts and dimension tables. Since the main goal of this modeling is to improve the data retrieval so it is optimized for SELECT OPERATION. The advantage of using this model is that we can store data in such a way that it is easier to store and retrieve the data once stored in a data warehouse. Dimensional model is the data model used by many OLAP systems.
Steps to Create Dimensional Data Modeling:
- Step-1: Identifying the business objective –
The first step is to identify the business objective. Sales, HR, Marketing, etc. are some examples as per the need of the organization. Since it is the most important step of Data Modelling the selection of business objective also depends on the quality of data available for that process.
- Step-2: Identifying Granularity –
Granularity is the lowest level of information stored in the table. The level of detail for business problem and its solution is described by Grain. - Step-3: Identifying Dimensions and its Attributes –
Dimensions are objects or things. Dimensions categorize and describe data warehouse facts and measures in a way that support meaningful answers to business questions. A data warehouse organizes descriptive attributes as columns in dimension tables. For Example, the data dimension may contain data like a year, month and weekday. - Step-4: Identifying the Fact –
The measurable data is hold by the fact table. Most of the fact table rows are numerical values like price or cost per unit, etc. - Step-5: Building of Schema –
We implement the Dimension Model in this step. A schema is a database structure. There are two popular schemes: Star Schema and Snowflake Schema.
Attention reader! Don’t stop learning now. Get hold of all the important DSA concepts with the DSA Self Paced Course at a student-friendly price and become industry ready.
Recommended Posts:
- Star Schema in Data Warehouse modeling
- Difference between Data Scientist, Data Engineer, Data Analyst
- Characteristics of Biological Data (Genome Data Management)
- Difference between Data Warehousing and Data Mining
- Difference between Data Privacy and Data Protection
- Difference between Data Privacy and Data Security
- Difference between Data Warehouse and Data Mart
- Data Mining: Data Attributes and Quality
- Data Mining: Data Warehouse Process
- Data Architecture Design and Data Management
- Difference between Data Lake and Data Warehouse
- Types of Sources of Data in Data Mining
- Data Reduction in Data Mining
- Data Transformation in Data Mining
- Data Abstraction and Data Independence
- Data Preprocessing in Data Mining
- HTML | <data> Tag
- What is Unstructured Data?
- What is Structured Data?
- Data Mining | Set 2
If you like GeeksforGeeks and would like to contribute, you can also write an article using contribute.geeksforgeeks.org or mail your article to contribute@geeksforgeeks.org. See your article appearing on the GeeksforGeeks main page and help other Geeks.
Please Improve this article if you find anything incorrect by clicking on the "Improve Article" button below.
