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

Written by Coursera • Updated on

Data marts and data warehouses are repositories that help organizations manage their data. Here are the key differences between the two tools.

[Featured image] A data analyst in a blue shirt works at a computer monitor

Data marts and data warehouses serve different purposes depending on the organization’s needs. Data analytics professionals use both to collect and manage data, so it can be analyzed for important business insights.

It is 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 will save you time and money.

This article will explain the difference between a data mart and a data warehouse, including 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 that sells a specific product (like toys). A warehouse may store toys for 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 one department’s data rather than that of the entire company.

Data mart vs. data warehouse: key differences

These are the key differences between a data mart and a data warehouse.

Data martData warehouse
Main definitionSubset of a data warehouseBig repository of data from various departments in an organization
SizeLess than 100 gigabytes (GB)More than 100 GB (usually terabytes)
ScopeSingle departmentEntire organization
Time to buildSeveral weeks or monthsMany months or years

What is a data mart?

A data mart is a subset of a data warehouse, though it does not necessarily have to be nestled within a data warehouse. Data marts allow one department or business unit, such as marketing or finance, to store, manage, and analyze data. Individual teams can access data marts quickly and easily, rather than sifting through the entire company’s data repository. 

The purpose of a data mart is to isolate data sets so that a team can request specific data based on what they need at that moment. 

Data mart use cases

To give you a better idea of when data marts are used, these use cases provide some context.

  • Marketing team’s brand positioning: A marketing team wants demographic information on customers who purchased a beauty product during summer of this year for better brand positioning the following year. Financial and operations data are unnecessary in this case, 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 to view the sales representatives’ performance at a retail company.

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

What is a data warehouse?

Data warehouses are large repositories of data collected and managed from various sources and departments within an organization. They store data historically. A data warehouse remains separate from a team’s operational systems, meaning they 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 

Data warehouse use cases

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

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

  • Systems integration: A company looking to improve their systems and 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 be agile in responding 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.

Data approaches: Bill Inmon vs. Ralph Kimball

There are two approaches that were pioneered by data warehouse experts Bill Inmon and Ralph Kimball, in which you decide whether the data warehouse or the data mart is built first.

The top-down approach, favored by Inmon, is that a data mart can be created from an existing data warehouse. Kimball’s bottom-up approach starts with business units creating their own data marts and, if the need arises, merging the data marts 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 day-to-day basis.

  • Data warehouse analyst: A data warehouse analyst researches and evaluates data from a data warehouse to make recommendations on improving data storage and reporting, as well as other business decisions. 

  • A senior-level data warehouse analyst manages a team and is in charge of data integrity and security.

  • Business intelligence analyst: A business intelligence analyst uses data marts or warehouses to develop company- or department-wide insights by building reports, dashboards, and visualizations using tools like Python, SQL, and Tableau.

  • Data warehouse engineer: A data warehouse engineer builds and manages data warehouse strategies. They might be responsible for setting project scopes, choosing the right software tools, and leading strategic solutions.

Other jobs that may play a role in using data marts or warehouses in a company might be IT professionals, software engineers, and data architects.

Learn the basics of data warehousing

Ready to learn about data warehousing? IBM’s professional certificate in Data Warehouse Engineering can fill in any gaps in knowledge so you can effectively implement and analyze data from data marts or warehouses in your organization. Start your free trial of Coursera today.


professional certificate

IBM Data Warehouse Engineer

Kickstart your Career in BI Engineering. Develop job-ready skills for an entry level role in Data Warehousing.


(236 ratings)

3,354 already enrolled


Average time: 8 month(s)

Learn at your own pace

Skills you'll build:

Relational Database (RDBMS), Business Intelligence (BI), Enterprise Data Warehouse (EDW), SQL, Extract Transform Load (ETL), Data Science, Database (DBMS), NoSQL, Database (DB) Design, Database Architecture, Postgresql, MySQL, Relational Database Management System (RDBMS), Cloud Databases, Python Programming, Jupyter notebooks, Shell Script, Bash (Unix Shell), Linux, Database Servers, Relational Database, Database Security, database administration, Extraction, Transformation And Loading (ETL), Apache Kafka, Apache Airflow, Data Pipelines, Data Warehousing, Cube and Rollup, Star and Snowflake Schema, cognos analytics

Written by Coursera • Updated on

This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

Learn without limits