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In this article, we will talk about the star schema data model. When should you use this to structure your data? What are its advantages over other data schemas? We’ll answer these questions and more in this article.
What Is a Star Schema?
The star schema is a popular approach for designing data warehousing and business intelligence data models. It uses dimensional modeling; in other words, it utilizes dimension tables with descriptive attributes that provide context for the measures in the fact table.
What Is Dimensional Modeling?
In dimensional modeling, a data warehouse is designed to store data in a way that improves its retrieval times. This is important for many data analytics applications.
There are two types of tables in dimensional modeling. The fact table contains the quantitative data or measures of the business, while the dimension tables provide context or descriptive attributes by which the measures are analyzed. For example, a sales data model could have a sales fact table with dimensions like time, product, customer, and geography.
What Is a Fact Table?
In the star schema data model, the fact table is the most important element of the entire schema. It is at the center of the model and generally contains only numerical data, or data that can be measured.
The fact table is connected to each dimension by the foreign keys, allowing the measures to be analyzed by different dimensions. Each row in the fact table represents a unique combination of the dimension attributes and their corresponding measure.
For example, a row in a fact table containing sales data might have a column representing the quantity of a specific product sold on a particular date to a specific customer in a specific region.
The purpose of a fact table is to enable fast and efficient querying of the data, making it possible to analyze large datasets quickly and efficiently. The fact table is usually very large and can contain many millions or billions of rows. This is why it is essential to have a well-designed data model for any application.
In short, the fact table in the star schema data model is where the numerical data or measures of the business are stored. It has foreign keys for each dimension and enables fast and efficient querying of the data. If you want to go in depth about the intricacies of fact tables, be sure to check out our article on fact tables in data warehousing.
What Is a Dimension Table?
A dimension table is the other important component of the star schema data model. It contains descriptive attributes that add context to the quantitative data in the fact table. Each dimension represents a specific attribute or viewpoint from which the data can be analyzed. Examples of dimensions for a sales data model can include time, product, customer, and geography.
Dimension tables are generally much smaller than fact tables, making them easier to query and analyze.
Similarly to the fact table, in the star schema data model, each dimension table is connected to the fact table through a foreign key. This relationship allows the fact table to be analyzed by different dimensions. For example, in a sales data model, the fact table could be analyzed by the product, customer, and time dimensions. This can be used to gain insights into sales trends by product, customer demographics, and sales performance over time.
In conclusion, a dimension table is the place where descriptive attributes are stored, attributes which add context to the quantitative data in the fact tables. If you want to go more in-depth, we have a great article on the types of dimensions in a data warehouse.
Why Use a Star Schema in Data Modeling?
The star schema data model's design optimizes query performance for analytical queries rather than transactional queries. This makes it easy to navigate and understand for business users with little technical knowledge.
For example, in a sales data model, some potential analytical queries that can be performed using the star schema data model include:
- Sales by product: Analyzing the sales of different products over a time period, such as monthly or quarterly sales.
- Sales by customer: Analyzing the sales made to different customers over a specific time period, such as monthly or quarterly sales by customer.
- Sales by region: Analyzing the sales made in different regions over a specific time period, such as monthly or quarterly sales by region.
By analyzing data in this way, businesses can make informed decisions and optimize their strategies for growth and success.
How Do You Build a Star Schema?
To build a star schema, there are generally 5 steps you need to go through. The following is a brief summary of each step:
- Identify business processes: The first step in building a star schema is to identify the key business processes that need to be modeled. This involves understanding the organization's data requirements and identifying the key metrics that need to be tracked.
- Select dimensions: Once the business processes have been identified, the next step is to select the dimensions that will be used in the model. Dimensions are the descriptive attributes that provide context for the measures in the fact table. For example, in a sales data model, dimensions might include time, product, customer, and geography.
- Create the fact table: The fact table is the centerpiece of the star schema and contains the quantitative data or measures of the business. The fact table is connected to the dimension tables through foreign keys, allowing the data to be analyzed from different perspectives.
- Define the relationships: Once the fact table and dimension tables have been created, the next step is to define the relationships between them. This involves creating foreign keys in the fact table that reference the primary keys in the dimension tables.
- Populate the tables: Once the schema has been designed and the relationships have been defined, the final step is to populate the tables with data. This involves importing data from various sources and transforming it to conform to the schema.
Examples of Star Schema Data Models
We’re now going to go over three example use cases where the star schema data model could be deployed.
Sales Data Model
A sales data model could be designed using a star schema to track sales metrics, such as revenue, quantity sold, and profit. The fact table would contain the sales data, while the dimension tables would provide context, such as product, customer, time, and geography dimensions.
Below you can see an example of what a star schema data model would look like for this scenario.
Financial Data Model
A financial data model could be designed using a star schema to track financial metrics, such as revenue, expenses, and profit. The fact table would contain the financial data, while the dimension tables would provide context, such as account, time, and department dimensions.
Below, you can see an example of how a star schema data model would look for this scenario.
Human Resources (HR) Data Model
An HR data model could be designed using a star schema to track employee metrics, such as employee turnover, training, and performance. The fact table would contain the HR data, while the dimension tables would provide details like the employee, department, and time dimensions.
In this scenario, a fact table named “Metrics” could contain different information related to the employee. The HR department would be interested in metrics related to employees’ work; examples of such metrics are turnover rate, training participation rate, and performance ratings. Some examples of the information that could be stored in this table are:
- Turnover Rate: The percentage of employees who left the company in a quarter (or other time range).
- Training Participation Rate: The percentage of employees who participated in training during a specific time period.
- Performance Rating: An average performance rating for employees during a specific time period.
- Time ID: A foreign key to a time dimension table that represents the time period for which the metrics are being measured (e.g. year, quarter, month, week, etc.).
- Department ID: A foreign key to a department dimension table that represents the department to which the employee belongs.
Other relevant metrics – such as employee absenteeism rate, employee satisfaction rate, employee engagement score, etc. – could also be included.
Below, you can see an example of how a star schema data model would look for this scenario.
If you want more examples of database schemas, feel free to check out our free resource on database schemas.
Advantages of a Star Schema
In summary, here are some advantages of using a star schema in data modeling:
- Improved performance: Queries will run faster on a star schema data model, which will enable analysts and business users to get answers to their questions quickly and efficiently.
- Simplified analysis: Because a star schema is intuitive and clear, the organization of data will make analysis easy for business users, which means they can identify trends and patterns that might otherwise be missed.
- Scalability: The star schema is able to handle large amounts of data, making it a great choice for businesses that deal with massive amounts of data that need to be analyzed quickly and combined in dynamic ways.
- Easy maintenance: Because the star schema has a simple design, it makes it easy to maintain and update the model, all while reducing the risk of errors and downtime of the overall system.
When a Star Schema Is the Right Choice
Now that you’ve made it to the end of the article, I hope that you have a better understanding of where a star schema can be used and what advantages it provides for data modeling and analysis. By organizing data into dimensions and measures, the star schema provides an easy way to analyze large datasets, allowing business users to make better decisions based on data.
Additionally, the star schema's scalable design makes it an ideal choice for situations where businesses need to store and analyze massive amounts of data. Plus, its ease of maintenance ensures that it can be updated and modified as needed.
Overall, the star schema is a powerful tool for any businesses that are looking to gain a deeper understanding of their operations and make decisions based on data. This can drive growth and success.
There are however scenarios where a star schema is not sufficient and you might need an extended version of this data model. In that case, be sure to check out the snowflake schema. You can learn more in our article that compares a star schema and a snowflake schema in data warehousing.
