The Critical Differences Between Data Marts and Data Warehouses

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As the volumes of data collected have grown exponentially, cloud storage infrastructure has kept pace. Companies can choose from data warehouses, data lakes, lake houses, and so on.


Data marts and warehouses are 2 of the most commonly used storage choices. Both offer different pros and cons, and your business goals ultimately dictate which one is best suited for you.

So how can you make the best choice? Here are the major differences between data marts and warehouses.

Cloud Computing
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Difference #1 – Definitions

Data warehouses and marts can be differentiated based on their names. Warehouses are large repositories of data gathered from different sources.

Typically, data volumes stored in warehouses are large. In contrast, a data mart is a subset of a data warehouse. Its usage is localized to a specific department or business unit.

Often, companies use data marts and lakes along with a warehouse. For instance, many companies use Snowflake (a data warehouse) along with a data lake such as Databricks. They then deploy a data mart as a single-use solution to address localized business needs. 

Thus answering the question of comparing solutions like Databricks vs Snowflake often comes down to your business needs. In addition, you must then figure out whether you’ll need a data mart for additional data handling. Marts help you quickly process and load data while warehouses are comparatively slow.

However, warehouses host a large volume of data compared to data marts. Also, you can define detailed schemas within a warehouse, whereas marts lend themselves well to less-complex definitions. Thus, if your data is varied and requires deep analysis, a warehouse is probably your best bet.

A data mart is a great option if you have several predefined data requests to address that need a specific set of variables.


Difference #2 – Scope

A data warehouse aims to serve the entire organization, as you’ve already learned. This aim results in design and processing conditions that create differences in everyday processes. For instance, warehouses accommodate data from several sources while marts host data from a small number of them.

As a result, warehouse sizes are large while marts are relatively small. Warehouses are very complex to install since data schema definitions and ETL processes take a while to define. In contrast, marts can be up and running in a few weeks once preliminary tasks have been executed.

Data marts have limited uses, as you’ve already learned. They’re not an efficient choice if you’d like to scale your data handling capabilities. Warehouses offer you flexibility when it comes to scaling since you can easily append new tables and schemas to accommodate new data.

However, a warehouse’s size often works against your goals. If your business experiences a massive change that requires data redefinitions, your current warehouse will form a roadblock to progress. You’ll most likely have to install a new one, something that will delay go-to-market times.


Marts are thus better suited to rapidly changing business conditions. However, even they have limits. If your underlying data analysis variables change often, your data mart isn’t going to help you very much. You could deploy multiple data marts easily to account for changing conditions, but at some point, you’ll need a warehouse to store and centralize your data.

Difference #3 – Analysis implications

Warehouses can store large volumes of complex data and this helps you run complex analytics on data. This doesn’t mean data marts don’t serve analytics purposes.

The context in which you need analytics matters. If your business needs complex modeling and intends to implement AI engines to crunch data, a warehouse is your best choice.

The downside of warehouse-driven analytics is that queries take a while to run. If your business faces constantly changing conditions, your analytics might be obsolete by the time results arrive. Thus, data marts are often used in such situations. However, you must be specific when using a mart.


For example, if your sales team needs instant insight into what is driving current customer purchasing trends, a data mart won’t help them since this analysis needs large data processing.

However, if they need insights into purchasing trends of specific products (units sold, locations, top-selling locations, product sales compared to other products, benchmarks, etc,) a data mart is a great choice.

Thanks to returning results quickly, your teams can make ad-hoc decisions quickly, impacting your business positively. The trick is to define which areas of your business you would like to focus on. If your focus extends beyond a single department or business function, a lake or warehouse will serve you better.

Any decision that is aimed at long-term performance-boosting needs a warehouse. A significant percentage of data gathered these days is unstructured. In such cases, warehouses and lakes are a better bet than marts.


Which is the best choice for you?

Ultimately, the choice of a data mart versus a data warehouse boils down to your business objectives and timelines. Both choices will serve you well, so take the time to pick which one suits you best.

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