Data Warehouse vs Data Mart: What Is the Difference?

What is the difference between a data warehouse and a data mart?

The main difference is scope. A data warehouse brings together data from many systems across the business and models it so that anyone can ask questions that cross departments. A data mart holds a narrower selection, shaped for one group of users or one subject area, such as sales performance, stock or HR.

A mart is often described as a subset of a warehouse. That is true for one common design, but not all of them. Some marts are built directly from source systems with no warehouse behind them. We cover that distinction below, because it decides whether marts help you or cause trouble later.

If you want the wider background first, read what is a data warehouse. This article concentrates on how the two compare and how they fit together. Our data warehouse services page covers what a build involves in practice.

How do they compare side by side?

Size is not the only difference. A mart can be quite large in data volume and still be a mart, because the word describes its purpose and audience rather than its storage.

How are warehouses and marts built: top-down or bottom-up?

There are two long-standing build philosophies, usually linked with two well-known data warehousing authors. You do not need to pick a camp, but it helps to know what each one trades.

Top-down

In a top-down build, you design and load the central warehouse first, with data from across the business. Marts are then created from it for each team. The benefit is consistency, because every mart starts from the same integrated data. The cost is that it takes longer before anyone sees a result.

Bottom-up

In a bottom-up build, you deliver one mart at a time, starting with the area that matters most, such as sales. Each mart uses shared dimensions, like customer and date, so that marts can later be combined. The benefit is speed. The cost is discipline: if the shared definitions are skipped, the marts drift apart.

Dependent and independent marts

• Dependent marts are fed from a central warehouse. They are easy to keep consistent, because they reuse the same cleansed data.
• Independent marts are built straight from source systems, with no warehouse behind them. They are quick to create, but each one repeats its own extraction and rules.
• Hybrid approaches start with a mart for a priority area, and grow it into a warehouse by adding sources and shared dimensions over time.

Our guide to data warehouse design explains how shared dimensions are planned so that later marts fit together.

When is a data mart enough?

A mart is a perfectly good answer in some situations. Building a full warehouse when you do not need one is a common and expensive mistake. A single mart is often enough when the following apply.

• One team owns the questions. For example, a sales team wants pipeline and revenue reporting and nobody else needs the data.
• You have a small number of sources. A CRM and an accounting system, for instance, can be modelled in a single mart without much integration work.
• You need a result quickly. A focused mart can reach users sooner than a company-wide programme.
• The data is not shared across departments. If finance, operations and sales never need to compare figures, there is little to integrate.
• You are proving value. A first mart can show what good reporting looks like before you commit to a larger build.

Many Power BI projects are effectively single marts. A well-structured semantic model for one subject area, fed by a few cleansed tables, does the same job. See Power BI services and BI software for SMEs for how smaller organisations approach this.

When do you need a central data warehouse instead?

A central warehouse earns its place when questions cross team boundaries or when several marts begin to overlap. Warning signs include the following.

• Two reports show different revenue. Sales and finance have each built their own logic, and nobody agrees which is correct.
• Several teams are extracting the same data. The same source tables are pulled and cleaned again and again.
• You need a single view of the customer. This requires joining data from marketing, sales, service and billing.
• You have many sources. Integrating ten systems is easier once, centrally, than repeatedly in each mart.
• You need history and audit. A central store can keep a consistent record of how figures looked at different dates.

If these sound familiar, start by mapping your current reports and showing where logic is duplicated. Combining data from multiple systems into one dashboard is often the first step, and it shows quickly whether a central model is needed.

What is data mart sprawl and why is it a risk?

Data mart sprawl happens when marts are created one by one, by different people, without shared definitions or an owner. Each mart makes sense on its own. Together they become hard to manage.

• Conflicting numbers. Each mart defines a customer, a sale or a margin slightly differently, so totals do not match.
• Duplicate effort. The same source is extracted and cleaned in several places, so a source change must be fixed in each one.
• Unclear ownership. When a mart breaks, nobody knows who maintains it, and users lose trust.
• Rising load on source systems. Many separate extracts can slow down the systems your business runs on.
• Security gaps. Copies of sensitive data end up in places that are not properly controlled.

Sprawl is preventable. Keep a register of every mart and model, with an owner and a purpose. Agree one source for each key definition. Review the list regularly, and retire anything that nobody uses. These habits cost little and save a great deal of rework later, when the business has grown and the original builders have moved on.

It is also worth asking whether a new mart is really needed. Often a new report on an existing mart, or a small extension to a shared table, does the job without creating another thing to maintain.

How do data marts relate to a central warehouse?

In a healthy design, marts are presentation layers over shared data. The warehouse integrates, cleanses and stores the detail. Each mart exposes the part that a team needs, in a shape that is easy to use.

The glue is shared, or conformed, dimensions. If sales and finance both use the same customer table and the same calendar, their marts can be combined without argument. This matters more than whether the marts are physical tables, views or Power BI models.

It also helps to agree a short list of governed measures, such as revenue and gross margin, and define each once. Teams can then build marts and reports freely, because the numbers underneath are the same. This is the practical idea behind improving data accuracy across departments.

For the platform choices behind a central warehouse, see data warehouse platforms. The same principles apply on Azure and other cloud environments.

What does this look like in practice? An illustrative example

Consider a hypothetical distribution company with a CRM, an accounting package and a stock system. The sales director wants a weekly view of pipeline and orders. Nobody else is asking for data.

A single sales mart is a sensible start. It takes customers and orders from the CRM and accounting system, adds a calendar and a product table, and feeds one set of dashboards. It can be delivered in a short project, and the sales team sees value quickly.

Six months later, finance wants margin by customer, and operations wants stock cover by product. Both need the same customer and product tables. If the company builds two more marts from scratch, it now has three versions of customer. If it moves the shared tables into a central layer first, the new marts reuse them and the numbers agree.

The lesson is not that the first mart was wrong. It is that the first mart should be built with shared dimensions in mind, so that growth is cheap rather than painful. Cleansing steps built in Power Query are a common place where this logic gets quietly duplicated, so keep an eye on it.

This is a made-up scenario, used only to show the pattern. Your sources and priorities will differ, which is why a short review is worth doing before any build.

How do you decide? A short decision list

Use these questions to decide where to start. Answer them with the people who will use the reports, not only the technical team.

1. How many teams need the data? One team points to a mart. Several teams point to a warehouse.
2. How many source systems are involved? Two or three can sit in a mart. Many sources justify central integration.
3. Do departments need to compare figures? If yes, shared definitions are essential, whatever you build.
4. How soon do you need results? A mart is faster. A warehouse needs more upfront design.
5. Who will own it after launch? Name an owner for every model, mart or warehouse, before building it.
6. Could this mart grow later? If so, design shared dimensions from the start, so it can become part of a warehouse.

What should you do next?

List your current reports, the systems behind them and the teams that rely on them. Mark every place where two reports should agree but do not. That list shows whether you have a single-team need or a cross-business one.

If you would like help, NetMonkeys' data warehouse team can review your reporting and recommend whether a mart, a warehouse or a staged approach fits. You may also find our guide to business intelligence consultants useful when planning who will build and run it.