Data Marts, Cubes, and Views: What Technology Leaders Need to Know
What Is a Data Mart?
A data mart is a focused collection of data built for a specific department, business function, or use case.
Think of a company’s data lake or data warehouse as a large distribution center containing information from across the enterprise. A data mart is the section organized specifically for one team, such as finance, sales, supply chain, or customer care.
For example, a retail sales data mart might contain:
- Orders and revenue
- Products and categories
- Customers and sales channels
- Discounts and returns
- Sales targets and margins
The data may come from several systems, including an ERP, e-commerce platform, CRM, and warehouse management system. The data mart brings that information together, cleans it, and organizes it around the questions the sales team needs to answer.
Data marts are especially useful when teams need trusted, business-ready data without navigating the complexity of the entire enterprise data environment.
What Is a Cube?
A cube is an analytical model designed to help users examine business performance from multiple angles.
Despite the name, a cube is not limited to three dimensions. It may allow users to analyze sales by product, customer, location, channel, salesperson, and time period.
For example, a retail leader might use a cube to answer questions such as:
- What were monthly sales by product category?
- How did gross margin vary by sales channel?
- Which distribution centers experienced the highest return rates?
- How did this quarter compare with the same quarter last year?
The cube defines important business measures, such as revenue, units sold, gross margin, and return rate. It also defines the dimensions users can apply to those measures, such as time, geography, brand, or channel.
Traditionally, cubes improved performance by calculating common totals in advance. Modern platforms may perform more of these calculations dynamically, but the underlying purpose remains the same: provide a consistent and intuitive model for business analysis.
A well-designed cube can become the shared language behind dashboards and reports. Everyone uses the same definition of revenue, margin, and other key metrics.
What Is a View?
A view is a saved query that presents data in a specific way.
In most cases, a view does not store another full copy of the data. Instead, it retrieves information from underlying tables whenever someone uses it. A materialized view is an exception because it stores calculated results to improve performance.
For example, an order table may contain hundreds of columns used by finance, operations, customer care, and fulfillment. A customer care view might expose only:
- Order number
- Customer name
- Order date
- Shipping status
- Tracking number
- Return status
The view simplifies access while hiding unnecessary fields and technical complexity. It can also apply business rules, join related tables, or limit users to authorized data.
Views are useful for creating clean, reusable access points. However, a large collection of poorly governed views can produce conflicting definitions and make the data environment harder to manage.
How Are They Different?
| Capability | Data Mart | Cube | View |
|---|---|---|---|
| Primary purpose | Organize data for a business area | Support multidimensional analysis | Present a defined slice of data |
| Usually stores data | Yes | Sometimes, depending on the technology | Usually no |
| Typical audience | Department or business function | Analysts and business users | Developers, analysts, and reporting tools |
| Common example | Supply chain data mart | Sales and margin analysis model | Open-orders view |
| Main value | Focus and governance | Consistent, fast analysis | Simplicity and controlled access |
The simplest way to remember the distinction is:
- A data mart determines which business data belongs together.
- A cube determines how that data can be analyzed.
- A view determines how selected data is presented or accessed.
How They Work Together
These tools are not competing choices. A mature data platform may use all three.
Consider an inventory reporting solution:
- A supply chain data mart combines inventory, purchase order, warehouse, and product data.
- Views create simplified datasets for available inventory, inbound inventory, and backorders.
- A cube or semantic model defines measures such as inventory value, days of supply, fill rate, and inventory turns.
- A business intelligence platform uses the model to deliver dashboards and self-service analysis.
Each layer has a distinct responsibility. Together, they make enterprise data easier to trust, govern, and use.
Choosing the Right Approach
Use a data mart when a department needs a governed, integrated source of business data.
Use a cube when users need to explore consistent metrics across multiple dimensions without recreating calculations in every report.
Use a view when you need to simplify access, combine tables, protect sensitive fields, or provide a reusable dataset without building another full data store.
The right choice depends on the business problem. Starting with the technology often creates unnecessary complexity. Start with the decisions people need to make, then choose the structure that supports those decisions.
Conclusion
Data marts organize information around the business. Cubes make that information easier to analyze. Views make it easier and safer to access.
When these components are designed with clear ownership and consistent business definitions, they turn a complex data platform into something far more valuable: a trusted foundation for decision-making.