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

A data mart is a database that serves a single business function, such as marketing or finance. On the other hand, a data warehouse can serve more than one function.This is what differentiates a data mart vs. a data warehouse. Data warehouses are databases that hold data marts and serve more than one business function in one place. Even with data warehouses in place, data marts serve a vital purpose. Siphoning data into a specific data mart allows users to query that data faster—it is much easier to query a limited set of data than the large datasets found in a data warehouse, and the response time is notably shorter.

Organizations will often deploy a data mart as a subset of their data warehouse, withdrawing applicable data into the data mart from the data warehouse. However, it is possible to create an independent data mart without the use of a data warehouse, as well as a hybrid data mart, where data is input from both the data warehouse and additional operational source systems. If an organization already has a data warehouse in place for storing information, deploying a data mart isn’t nearly as resource-intensive. Dataversity estimates the cost of a data mart at $10,000 (compared to upwards of $100,000 for a data warehouse) and the setup time between three and six months. 

Types of of data marts

The varying categories of data marts are largely determined by how the data mart does or doesn’t interact with a data warehouse.

  • Dependent. These data marts pull from a single data warehouse. In this approach, centralized data is stored and strategic chunks when they’re needed for analysis.
  • Independent. As you might guess, independent data marts don’t use a data warehouse. These are often used by smaller organizations which pull the data from other internal or external sources before analyzing.
  • Hybrid. Again an intuitive title, hybrid data marts use data warehouses as well as other data sources.

Data mart and data warehouse vs. data lake

Besides understanding data warehouses vs data marts, it’s useful to see how data lakes compare to these options. Increasingly, organizations are trading in their use of data warehouses and data marts for a modern alternative: the data lake. James Dixon, CTO of Pentaho, coined the term “data lake” and explains the difference between a data warehouse/data mart and a data lake with the following analogy: “If you think of a data mart as a store of bottled water—cleansed and packaged and structured for easy consumption—the data lake is a large body of water in a more natural state.” While a data mart and data warehouse demand structured and specific data sets, a data lake can handle a wide range of complex data, such as web server logs, sensor data, social network activity, text and images. In that sense, the data lake offers more opportunity for users to explore and increased flexibility to make changes to models and queries. 

Whether an organization has implemented data marts, data warehouses or a data lake, in all instances, these systems are increasingly moving to the cloud. Organizations are eager to take advantage of the increased efficiency and lowered costs that the cloud offers, which influences these business decisions. It should be made clear, too, that setting up a cloud data storage architecture is not an either/or situation; data marts and data lakes can be integrated together to serve both the rigid data needs of a certain business function and the overall desire to store an increased amount of complex data.


Data preparation for a data mart or data lake

Whether your organization relies upon singular data marts, a large data warehouse, a data lake or some combination of all three, preparing data from these systems for analysis is a consistent need. This is especially true of a data lake, where data is stored in its raw form and often requires significant transformation to arrive at a refined format for analysis. However, even when working with structured data from a data mart or data warehouse, the data will almost always have to be further refined in order to fit the requirements of a given analytics project. 

The extract, transform, and load process (ETL process), and ETL tools, have been the de facto way to move and transform data within data warehouses and data marts since the onset. But these tools have struggled to extend support to the self-service agility required of modern analytic projects. In short, these technologies were never designed to be put into the hands of the business analysts, often resulting in week or month-long lag times for IT teams to act upon the requests of their business counterparts. However, this doesn’t mean that organizations are abandoning ETL tools en masse—these tools still adequately allow IT organizations to move data from various sources—but rather are adopting a complementary platform for their business analysts. A member of any department, including business analysis, can use new complementary tools because they’re designed to be simple to access and to use. 

Trifacta is routinely recognized as the leader in data preparation and was specifically designed with end-users in mind. The Trifacta data preparation platform presents representations of data in the most compelling visual profile, and simply selecting certain elements of the profile immediately prompts intelligent transformation suggestions. It allows users to work with large, complex datasets and reduces the time spent preparing data by up to 90%. To learn how you can use Trifacta to prepare data from a data mart, data warehouse or data lake, schedule a demo of Trifacta today.