Data Mart vs. Data Warehouse: What’s the Difference?

Data marts and data warehouses serve different purposes depending on your organization’s needs. More specifically, a data warehouse stores large amounts of an organization’s information, making it easily accessible for analysis, while a data mart, a subset of a data warehouse, keeps the data for a specific section of your company, such as the marketing or finance department. 

It's essential to understand when and why you’d choose to use a data mart over a data warehouse and vice versa. Implementing these systems in a business involves planning, maintenance, and data analysis. Choosing the right tool can save you time and money because data analytics professionals use both data constructs to collect, manage, and analyze data to help businesses make strategic decisions.

Continue reading to discover the difference between a data mart and a data warehouse, including their use cases and the careers that utilize them.

Difference between a data mart and a data warehouse

Let’s remove the word “data” from these concepts for a second. You can think of a mart as a store with only one product (like toys), while a warehouse may store toys for a retailer like Toys "R" Us, but it may also supply swing sets and swimming pools to Home Depots and Wal-Marts across the country.

In short, a data mart is simpler than a data warehouse, storing data from one department rather than the entire company.

Data mart vs. data warehouse: Key differences

Although data marts and data warehouses share many similarities, understanding the essential differences can help you decide which to use and when each is most appropriate. Check out some primary differences between a data mart and a data warehouse.

What is a data mart?

A data mart is a subset of a data warehouse, though it does not necessarily reside within a data warehouse. Data marts allow one department or business unit, such as marketing or finance, to store, manage, and analyze data. As a result, individual teams within your organization can access data marts quickly and efficiently rather than sifting through your entire company’s data repository. 

A data mart aims to isolate data sets so that a team can request specific data based on what they need at that moment. 

Data mart use cases

Organizations use data marts to analyze department-specific information quickly and inform their decision-making. To better understand when to use data marts, consider a few common use cases:

• Marketing team’s brand positioning: A marketing team wants demographic information on customers who purchased a beauty product during the summer of this year for better brand positioning next year. In this case, financial and operations data are unnecessary, so a data mart is more fitting.

• Sales representatives' performance tracking: A sales team can use a data mart to combine month-over-month and year-over-year data in one dashboard so they can review the performance of your retail company's sales representatives.

• Shipping efficiency: In a shipping department, a data mart can track the total time and cost from the moment a customer places an order until they receive the delivery. In this case, a shipping data mart can interact with the sales department data mart to analyze overall shipping efficiency and cost.

What are the three types of data marts?

The three types of data marts are dependent, independent, and hybrid, which combines the previous two.

A dependent data mart relies on a data warehouse to function. Basically, the data is first deposited into a data warehouse and then distributed into a specific data mart. Alternatively, an independent data mart can perform as a standalone entity because it does not require a data warehouse to function. Essentially, these independent data marts become miniature data warehouses, allowing the department to operate them in a way that works best. Finally, depending on your organization’s needs, you might find that a hybrid system, which combines the two, offers an ideal approach to data mart storage.

What is a data warehouse?

A data warehouse is a large, central repository of data collected and managed from various external data sources and departments within an organization. These units store historical data, allowing users to access information from application log files and transaction applications. A data warehouse remains separate from a team’s operational systems, meaning it can be manipulated and viewed using queries as needed to conduct enterprise-wide data analysis.

Read more: What Is a Data Warehouse? Definition, Concepts, and Benefits 

Enterprise data warehouse use cases

Sometimes, having all your data in one place is more beneficial to your bottom line. These use cases illustrate when you should use a data warehouse instead of a data mart. 

• Systems integration: A company looking to improve its systems and business processes can use security devices, smartwatches, and other data-driven technologies to predict future trends and patterns using historical data. This can help deliver metrics and reports that enable teams to respond nimbly to changes.

• Centralized data to drive impact or profit: A health insurance company reporting on profitability needs a centralized data warehouse to gather information from sales, marketing, finance, and operations. Data warehouses allow companies to build dashboards to visualize this data.

• Company-wide performance evaluations: A retail company can use data warehouses to evaluate team performance across the company. Business intelligence analysts can create dashboards and reports based on customer value and usage patterns to evaluate marketing, sales, and customer service teams.

Data approaches: Top-down vs. bottom-up

Data warehouse experts Bill Inmon and Ralph Kimball pioneered two approaches for structuring data, in which you decide whether the data warehouse or the data mart is built first.

Inmon's top-down approach involves creating a data mart from an existing data warehouse. Kimball’s bottom-up approach starts with business units creating their own data marts and, if necessary, merging them into a centralized data warehouse. Both Inmon’s top-down and Kimball’s bottom-up approaches are perfectly valid.

Careers in data marts and warehousing

Because these tools are central to making data-driven business decisions, there are several careers that work with data marts and data warehouses on a daily basis. In fact, according to the US Bureau of Labor Statistics, certain data-positions are projected to grow five percent faster than average for jobs between 2021 and 2031 [1].

1. Data warehouse analyst

Median annual US salary: $107,402 [2] 

A data warehouse analyst researches and evaluates data from a data warehouse to make recommendations. In this position, you'll look for ways to improve data storage, reporting, and other business functions and strategic decisions.

2. Senior-level data warehouse analyst

Median annual US salary: $123,989 [3]

Data warehouse managers or specialists manage a team of junior-level analysts and are in charge of data integrity and security. In this higher-level job, you’ll also optimize data models, optimize workflows, and construct data warehouses. 

3. Business intelligence analyst

Median annual US salary: $99,684 [4]

A business intelligence analyst uses data marts or warehouses to develop company—or department-wide insights. In this role, you'll build reports, dashboards, and visualizations using tools like Python, SQL, and Tableau.

4. Data warehouse engineer

Median annual US salary: $107,811 [5]

A data warehouse engineer builds and manages data warehouse strategies. You will likely participate in setting project scopes, choosing the right software tools, and leading strategic solutions.

Other jobs that may involve using data marts or warehouses in a company include IT professionals, software engineers, and data architects.

Data lake vs. data warehouse vs. data mart

Wondering what the difference between a data warehouse, data lake, and a data mart is?

The three share some similarities, including their roles as solutions to support businesses in data storage. Data lakes offer a central location to store data, much like data warehouses. However, unlike warehouses, lakes store data regardless of size or complexity. Data lakes allow you to store semi-structured and unstructured data without needing preprocessing. Additionally, although data warehouses offer fast performance, data marts provide storage power at reduced costs.

Learn more about data warehousing and analytics

Understanding the difference between a data warehouse and a data mart can help you analyze your data more effectively and efficiently, leading to valuable insights about your business.

Continue your learning journey on Coursera with programs like IBM’s Professional Certificate in Data Warehouse Engineering, which help you effectively implement and analyze data from data marts and warehouses in your organization.

In the Google Advanced Data Analytics Professional Certificate, you'll discover in-demand skills like statistical analysis, Python, regression models, and machine learning.