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

Written by Coursera Staff • Updated on

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

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Data marts and data warehouses serve different purposes depending on the organisation’s needs. Data analytics professionals use both to collect and manage data so it can be analysed to help businesses make strategic decisions.

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.

Read on to learn the difference between a data mart and a data warehouse, including use cases and the careers that utilise them.

Difference between a data mart and a data warehouse

You can think of a mart as a store that sells a specific product (like toys). A warehouse may store toys for a retailer like FirstCry, but it may also supply swing sets to DMarts nationwide.

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

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 organisation
SizeLess than 100 gigabytes (GB)More than 100 GB (usually terabytes)
ScopeSingle departmentEntire organisation
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 one. Data marts allow one department or business unit, such as marketing or finance, to store, manage, and analyse data. Individual teams can access data marts quickly and easily rather than sifting through the entire company’s data repository. 

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

Data mart use cases

Highlighted below are some everyday use cases illustrating the usage of data marts:

  • 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 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 when 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 analyse overall shipping efficiency and cost.

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 organisation. They store data historically. A data warehouse remains separate from a team’s operational systems, meaning it’s possible to manipulate and view them using queries to conduct enterprise-wide data analysis.

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 user should employ 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. It can help deliver metrics and reports that enable teams to remain nimble in the face of changes.

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

  • Company-wide performance evaluations: A retail company can use data warehouses to evaluate 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.

Data approaches: top-down vs bottom-up

Data warehouse experts Bill Inmon and Ralph Kimball pioneered two approaches to determine the sequence of building data warehouses and data marts.

Inmon favours a top-down approach, in which 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 necessary, merging them into a centralised data warehouse.

Both Inmon’s top-down and Kimball’s bottom-up approaches are perfectly valid.

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Careers in data marts and warehousing

Because these tools are central to making data-driven business decisions, several careers involve daily working with data marts and data warehouses. They include the following:

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

  • Senior-level data warehouse analyst: Data warehouse managers or specialists manage a team of junior-level analysts and manage 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 visualisations 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 involve using data marts or warehouses in a company include IT professionals, software engineers, and data architects.

Learn more about data warehousing.

Data marts and data warehouses offer storage solutions that aid in analysing business data. By understanding the distinctions between the two, you can think about which option best meets your company's preferences and requirements.

IBM’s Professional Certificate in Data Warehouse Engineering can fill in any gaps in knowledge so you can effectively implement and analyse data from data marts or warehouses in your organisation. Alternatively, in Google's Google Advanced Data Analytics Professional Certificate, you'll learn in-demand skills like statistical analysis, Python, regression models, and machine learning. Start your free trial of Coursera today.

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