Understanding Star Schema Data Modeling

Key takeaways

The star schema data model organizes data in a database to help users interpret, perform analysis, and report on the information.

• The two distinct parts of a star schema are the fact table and the dimension table. 

• Several benefits of a star schema include optimized performance for basic queries, easy-to-understand data analysis, and compatibility with online analytical processing (OLAP) systems.

• You can use a star schema in business intelligence to simplify queries and efficiently organize data.

Discover more about the parts of star schemas, how they work, their various use cases, and how they compare to the snowflake schema and third normal form (3NF). If you’re ready to build your data analysis skills, enroll in the Google Data Analytics Professional Certificate. In as little as six months, you can learn about data cleansing, data literacy, data structures, data visualization, and more. 

What is a star schema data model?

To help users interpret, perform analysis, and report on the information contained within a database or relational data model, the star schema data model organizes your data effectively and succinctly. Ralph Kimball introduced star schemas in the 1990s as a new data model with applications in data warehouses, databases, and various additional tools [1]. The star schema model optimizes querying with the added function of updating your information within your warehouses or databases and tracking the history of these updates.

Star schemas feature fact tables and dimension tables linked together, allowing you to filter and slice your data in any way you need. The design of the star schema model leads to a denormalized data structure, requiring fewer joins between your tables compared to other schemas. This overall structure promotes faster querying, readability, and improved performance. Users typically apply it to data warehouses for financial reporting, marketing analysis, and inventory management.

Overall, star schemas are simple to understand and build for users because their architecture does not rely on normalized data in their dimension tables. The star schema's denormalized data structure leads to fewer tables in the model. While this enables enhanced performance and readability, it slows system updates and can cause data redundancy. 

Is star schema still relevant?

Many companies use the star schema data model to organize their data in a readily accessible fashion. As companies collect more and more information daily, effective data modeling using star schema, for example, can help spur growth within a business and build an advantage over competing organizations. Data models serve to outline and illustrate how your systems utilize, store, and organize information. They also showcase the relationships between your data and how they interact within your databases.

With applications to build data warehouses and data marts and enhanced query performance, the multidimensional star schema data model is a popular choice over other models today.

Parts of a star schema

If you consider the actual structure of a star schema, it resembles its name. The central fact table is in the middle of the schema, connecting with an unlimited number of dimension tables branching out from the center. The two distinct parts of a star schema to learn about are the fact table and the dimension table, and their importance. 

Fact table

The fact table within star schemas contains numerical information and dimension attribute values. The facts can be qualitative or quantitative measures of your business processes. Examples of information stored in fact tables include sales data, market share, revenue, employers, customers, or inventory. 

Dimension table

The values contained in dimension tables come in two different types of columns. One column stores unique identifiers within the data set, and the next provides descriptive characteristics of the information in your database. A common dimension table found within star schema models is a date dimension table, which acts as a column to showcase dates. 

The star schema can have many dimension tables within the data model. These dimension tables link back to a central fact table through joins based upon surrogate keys, which include primary and foreign keys. The linkages between a central fact table and each present dimension table represent “many-to-one” relationships. This means that dimension tables hold many descriptive values that relate to a unique numerical value on the fact table. 

Read more: What Is the Difference Between Fact and Dimension Tables?

How does a star schema work?

To understand how a star schema works in practice, it is helpful to walk through an example of how an organization may utilize this model within its databases and data warehouses. Suppose a clothing company leverages the star schema model in its data warehouse to store all critical information related to its sales. Within the data warehouse, a fact table would contain all data detailing their sales. This table could include attributes such as product ID or customer ID and numeric values that do not correlate to any other row of information in the database. 

Multiple-dimensional tables describing the sales in more detail would be connected to the fact table. Each dimension table represents its own attributes, such as customers, products, geographic region, and time. The properties within the fact table relate to a corresponding dimension table that contains additional information about the specific property. For example, the product ID attribute in the fact table connects to the product dimension table, where the details about the company’s products live. 

Illustration of a star schema

Microsoft Learn [2]

Star schema usage scenarios

Analysis and running queries are popular ways to use star schemas. Star schemas simplify the types of queries you use by having fewer connections or joins between the tables within the schema data model. This enhances query development and allows queries to be completed more quickly.

Business intelligence projects commonly deploy the star schema data model to simplify queries and efficiently organize data. Star schema is the recommended model to use when developing for Power BI due to its efficient performance capabilities and ability to simplify Data Analysis Expressions (DAX). 

The benefits you gain from the essential characteristics and overall structure of star schema often apply when limited maintenance and enhanced performance are the main focus. Companies or organizations frequently handling inventory, finances, or marketing analysis often opt to use star schema for their data marts and data warehouses. 

Pros and cons of using a star schema model

Star schemas have many benefits, such as operational simplicity. If you’re considering the star schema data model for your work, a few benefits to keep in mind include:

• Optimized for basic queries

• Allows straightforward data analysis that is easier to understand 

• Strong query performance

• Simple relationships

• Compatible with online analytical processing (OLAP) systems

Although the star schema model provides many pros and is the preferred data model choice in various situations, including Power BI, you may encounter some potential drawbacks with this data model. The possible limitations of the star schema model are:

• Complexity related to updating information.

• Limited set of possible queries

• Narrow analytical power

• Data redundancy

What are the 4 types of data models?

A star schema is a multidimensional data model that organizes data across dimensions, such as location, time, and item, for more efficient relationship analysis. Three additional categories include conceptual, logical, and physical. 

Conceptual models provide an overall view of what the data system contains, establish the organizational method for the data, and connect it to specific business rules and concepts. The logical model takes the more abstract conceptual version and adds to it, incorporating more detailed information, such as the associations between data groups and specific attributes. The physical model translates the logical model’s information into a technical schema for a database that can physically store the data. The physical model provides a comprehensive representation of data storage, organization, and management. 

Star schema vs. snowflake schema vs. third normal form: How do they differ?

Data warehouses can utilize various forms of schemas or dimensional models, such as the star schema, to organize the structure for storing the company’s data. Each model contains some similarities but also some differences. Three common schemas to compare and contrast are the star schema, the snowflake schema, and the third normal form (3NF).

Snowflake schemas use a “branched-out” structure, placing general data and information in the middle of the schema. As you move along the branches towards the outside of the schema, the data becomes increasingly specific. The snowflake schema also breaks down its dimensional tables into further subdimensions, representing the more specific information on the edge of the schema. 

The star and snowflake schema models are similar in that they both have applications to big data systems. These schemas thrive with large quantities of data you might otherwise have difficulty organizing and maintaining. A key difference between star and snowflake schemas is that snowflake schemas provide more granularity due to their inclusion of subdimensions and provide higher integrity for the data in the system. 

The 3NF model minimizes data redundancy and clears all anomalies related to updates, deletions, and insertions from a database or data warehouse. The 3NF process effectively normalizes databases and has become the typical criterion for classifying a database as fully normalized. One key difference is that the 3NF model features additional data tables that join to the center, yielding more complex queries compared to the other two schemas. 

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