Dimensional Data Modeling: Examples, Schema, & Design
In most applications, data is often available in a convoluted format, which is difficult to interpret. Organizing this data in a structured manner makes it seamless to perform complex analysis to generate actionable business insights. Multiple modeling methods enable the structuring of raw data. Among the most popular processes is dimensional data modeling.
This article discusses dimensional data modeling with its components, applications, design patterns, and the steps to implement this strategy.
What Is Dimensional Data Modeling?
Dimensional data modeling is a conceptual data modeling method that enables you to organize data in a comprehensible and usable form. It is optimized for analysis and reporting, allowing you to analyze large amounts of data. Representing information in a well-structured manner can provide you with the capability to generate impactful insights from complex datasets.
Why Use Dimensional Data Modeling?
- Simple Querying: Dimensional data modeling offers an intuitive schema that enables you to query data almost effortlessly. You can use this data to make data-driven business decisions.
- Better Performance: The database schema produced by dimensional data modeling is optimized for better performance than traditional normalized models. In a dimensional data model, the data is denormalized. This eliminates the requirement of complex joins, minimizing data redundancy and allowing operations on numbers rather than using texts.
- Flexibility: The dimensional data models are robust in nature yet adaptable to changes, which can be made according to your business requirements. The flexibility offered by these models makes it easier for you to consolidate data from different platforms.
- Enhanced Data Quality: Dimensional data modeling significantly improves data quality by simplifying the format of complex data, reducing efforts while performing advanced analytics. It aids in eliminating errors and inconsistencies in your data, refining it into a well-structured format.
Components of Dimensional Data Model
Fact tables, dimension tables, and database keys are the most crucial components when discussing dimensional data modeling. Let’s explore each element in detail.
Facts and Fact Tables
Facts are quantitative data elements that represent various business metrics, such as performance or outcomes. For example, metrics like revenue, profit, and product quantity could be considered facts.
The fact table stores the business facts data with foreign keys that are referenced to the dimension table. Each row in the fact table highlights the recorded transaction, while columns display attributes or events, like total sales, quantity, and date.
Dimensions and Dimension Tables
Dimensions are descriptive data elements that enrich the fact elements. For example, attributes like product name, customer name, and customer address describe the data stored in the facts table. The dimension table holds the dimensional factors of the data, representing additional information about the transaction in each fact table row.
Keys
Both the fact and dimension tables are linked to one another using a set of keys. These keys include the primary key and the foreign key. The primary key is part of the dimension table that contains unique, not-null information about each individual component of the dimensional data. The foreign key, on the other hand, usually references the primary key of the dimensions table. This allows you to join different tables together to get a holistic view of data.
Relational Vs. Dimensional Data Models
Here’s a table demonstrating the key differences between relational and dimensional data models:
Dimensional Data Modeling Design Patterns
It is beneficial to understand the schema design patterns that you can use to implement dimensional data models. These patterns define the arrangement of facts and dimensions within a database to achieve optimized performance. Let’s discuss the general dimensional modeling schema designs.
Star Schema Design
Star schema is an architectural design that resembles a star-like structure. In this design pattern, a fact table is located at the center of the star and surrounded by multiple dimension tables. The fact table is normalized to reduce redundancy, while the dimension tables are denormalized to foster better performance when executing advanced analytics.
Snowflake Schema Design
Snowflake schema design is an extension of the star pattern, which contains a greater number of dimensions. In this design pattern, the dimensions are normalized and further divided into sub-dimensions. This results in the development of additional tables that store specific attributes within a dimension, creating a hierarchical model.
With the increase in normalization, the Snowflake schema design reduces redundancy and enhances data integrity. You can refer to Star schema vs Snowflake schema to understand the in-depth differences between the two prominent schema design patterns.
Fact Constellation Schema Design
Fact constellation schema design is a useful pattern for representing multi-dimensional models. It defines a collection of fact tables connected to common dimension tables. The name ‘fact constellation’ comes from its design, where multiple star schemas are interconnected to form a galaxy. This is the reason why the fact constellation design pattern is also known as the Galaxy schema. While this pattern is difficult to manage, it provides a higher flexibility to work with complex datasets.
How to Implement Dimensional Data Modeling?
To implement dimensional data modeling, follow these steps:
Step 1: Identify Appropriate Business Process
The first step involves identifying the business process that you wish to monitor. It could be anything, from customer satisfaction scores to credit card transactions. This step also includes defining metrics like key performance indicators (KPIs) and dimensions. For example, an e-commerce dataset might have revenue and units sold as KPIs, whereas product name and store name are dimensions.
Step 2: Select the Granularity of the Fact Data
Granularity is the lowest level of information stored in a table. Declaring a common grain depends on the amount of data and the level of detail for transactional-level information. It is often considered beneficial to begin with the finest grain of data to store each transaction. For example, the total number of products sold can be defined with respect to daily, weekly, monthly, or yearly sales.
Step 3: Determine Dimensions
Create a separate dimension table by determining the attributes for each transaction. Each row in the dimension table must have a unique and not null primary key column that differentiates every record. Defining a dimension table is vital in determining how data warehouses store contextual information. For example, product name, customer name, customer ID, and date of birth can be a part of the dimensions table.
Step 4: Consolidate the Facts
The rest of the remaining metrics are your fact data, including sales amount and quantity sold. Most of the data stored inside a fact table contains numeric information. Each row in the fact table must have a foreign key that references the dimension table. These foreign keys can be used to join different tables according to your specific analytical requirements.
Step 5: Build a Schema
Finally, you can start building a schema to store data. The design of the schema must be such that it ties together all the dimensions and fact tables. Depending on your specific requirements, you can choose the architecture from star, snowflake, and fact constellation schema design.
Common Challenges with Dimensional Data Models
Although there are multiple benefits of dimensional data models, there are some complexities you might encounter while working with them. Some common challenges are:
Data Quality Issues: The accuracy of dimensional data models depends on the quality of the data used to create them. To develop an effective dimensional data model, ensuring the availability of quality data becomes a critical aspect.
Historical Data Tracking: It becomes difficult to track historical data changes, as dimensional modeling generally focuses on current data. To overcome this challenge, you might have to apply different types of slowly changing dimensions in data warehouses.
Performance Tuning: Continuous tuning is crucial to optimize query performance and ensure the effectiveness of the model in a data warehouse. This might be time-consuming and require manual intervention to monitor the process.
Data Integration: Dimensional data modeling requires data integration from multiple sources, which can be complex to perform. This is especially applicable if you follow a manual process of moving data to the data warehouse, as it requires extensive technical expertise. However, you can use tools like Airbyte to streamline data migration.
Airbyte is a data replication tool that allows you to move data from multiple sources to the destination of your choice. Offering over 550 pre-built connectors, it enables you to transfer data to any platform, including but not limited to popular data warehouses, such as Snowflake. If the connector you seek is unavailable, Airbyte offers options to create custom connectors using No-code Connector Builder, Low code CDK, and Language-specific CDK.
Let’s explore some amazing features Airbyte offers:
- AI-Powered Connector Builder: The Connector Builder comes with an AI-assist functionality that reads through your preferred platform’s API documentation and auto-fills most configuration fields.
- Change Data Capture (CDC): CDC lets you identify incremental changes made to the source data and replicate them to the destination system. This helps keep track of updates and maintain data consistency.
- Enterprise Edition: Airbyte also has an Enterprise Edition that allows you to handle large-scale datasets in your virtual private cloud (VPC). With robust features, including RBAC, multitenancy, personally identifiable information (PII) masking, and enterprise support with SLA, this version provides you with more control and security.
Dimensional Data Model Use Cases
- Banking Sector: Utilizing the dimensional data model in the banking domain facilitates the storage of transactions with customer data. The facts table stores the transactional data, whereas the dimension table stores customer information, account type, and transaction type. This enables analysis of transactions and detection of frauds.
- Healthcare System: Applying dimensional data modeling principles in a healthcare system allows the identification of drugs and specific treatment procedures that best suit patients. In this system, the fact table stores treatment data like body temperature and pressure, and the dimension table holds patient and physician information.
- Media and Entertainment: A dimensional data model analysis aids in determining user engagement. Building a fact table with attributes like views and watchtime and dimension tables with content type, user name, and device information supports tracking content performance.
Conclusion
Understanding the concept of dimensional data modeling is crucial for applications that rely on heavy analytical processes. By splitting the data tables into fact and dimension tables, you can structure data, making it straightforward to interpret. However, before performing the implementation steps, it is necessary to know about the challenges that come with dimensional data models. This can significantly enhance your data modeling journey.