A Guide to Dimensional Modelling in Data Warehouse

Dimensional Modelling in Data Warehouse

Dimensional Modelling is one of the efficient and optimum ways to store data in the database. It allows for better management and optimization to retrieve data and eliminates redundancy. This blog covers everything to help you understand more about dimensional modeling in data warehouses.

Data storage and accessibility is a major task these days as businesses deal with a large number of data on a day-to-day basis. Be it a SaaS product or an e-commerce platform, data handling is required on a large scale while taking care of various aspects like- redundancy, storage, retrieval efficiency, etc. As Data Warehousing deals with the concept of storing and retrieving crucial data out of large storage to enable businesses to make business decisions insightfully and factually, the data modeling technique helps businesses to store data in such a way that enables the efficiency of data retrieval to get optimized for better performance.

While the data warehouse concept might demand various aspects related to storing and retrieving data, dimensional modeling in a data warehouse helps make the data retrieval quite effective and efficient.

Dimensional Modelling offers numerous advantages to storing data in the database. It allows easy storage and retrieval of data to use for further analysis and reporting. It stores data in the form of Dimensions and Facts to ensure optimum efficiency in terms of data structure and performance.

Dimensional Modelling: Understanding Dimensions and Facts

The modern world information is linked with ROI and this is the reason why databases need to move to the contemporary scenario to meet the market demands. Dimensional Modeling is an effective way to synchronize and improve the process of data storage and retrieval, it makes data organization smooth and better in terms of accessibility.

This technique stores data in the form of facts and dimensions. The facts are real-world data linked with the quantitative aspect of information while the dimensions relate to contextual business processes or events.

While talking in a real-world scenario, the piece of information related to metrics like sales or downloads is considered as facts while dimensions can be broader aspects or events linked to those facts such as employees, campaigns, customers, etc. In this case, The facts have foreign key relationships with dimensions in data modeling.

Features of Dimensional Modelling in Data Warehouse

Dimensional Modelling offers unique aspects of data analysis and varied features which are as follows:

1. Improves Understandability of Data: Dimensional Modelling offers an easy way to design data schemas and databases that help in better interpretation of data stored in the data warehouse. The relationship between facts and dimensions can also be easily identified and established which helps in simplified and effective analysis of data.

2. Data Quality: To ensure data quality, the facts, and dimensions are linked with a foreign key ensuring referential integrity. It eliminates data redundancy and ensures data quality before loading into the database.

3. Optimise Data Performance: The dimensional modeling ensures better data organization enabling faster performance in terms of data retrieval. It organizes data in a manner that utilizes less storage and enables faster retrieval of data from a large database.

4. Flexibility: The data is a crucial aspect of any business and it should always have the scope to make updates as per the business requirements. In such a scenario, various new entities can be added to the database schema and changes can be incorporated into the central repository of the database. In Dimensional Modelling, this feature becomes a big advantage

Designing a Dimensional Model: Step-by-Step Guide

There are various steps involved in dimensional modeling which are as follows:

1. Identify the business process

The first step is to identify which business process you want to use to design a dimensional data model. There could be multiple processes and you need to identify which suits best in your case. For example, It can either be sales tracking or order management.

2. Identify Facts and Dimensions in Data Model

Since facts and dimensions are crucial aspects of the dimensional data model, you need to identify business facts associated with various dimensions. For instance, in sales tracking, you want to measure the sales which can be referred to as facts while dimensions can be store, location, products, etc. The dimensions offer in-depth insight into the business process.

3. Defining Granularity for Fact tables

Granularity can be referred to as the lowest level of information stored in a database. It can be bifurcated into daily, monthly, yearly, or weekly. This step helps in identifying the normalization that can be achieved through the data and other aspects such as – incoming frequency, data storage, storage cost, etc. For example, the sales profit is tracked daily, in this case, the granularity will be daily.

4. Tie Up Facts and Dimensions into Schema

The schema in data warehousing is referred to as a table structure aligned with data contained in various tables in the database. The two common types of schemas used in data warehousing are star schema and snowflake schema. These are schema designs that are used to bifurcate facts and dimensions into a separate table.

Limitations of Dimensional Modelling

Dimensional Modelling in a Data Warehouse is one of the preferred ways to store data but it may pose some limitations such as understanding of domain is required while creating the schema and it may also become challenging to load data from various operating systems. Despite these issues, dimensional data modeling can be a boon to your business.

Conclusion

Dimensional Data Modelling is no doubt an effective way to store a large chunk of data and can help organizations to achieve optimal performance in retrieving the data in a better way. It gives organizations an enhanced approach to making data accessible without any major challenges.