Data mart

What is data mart?

A data mart is a storage repository focused on a specific line of business, e.g. a marketing data mart that stores centralized data of the marketing department. This way, marketing users don’t have to search the organization-wide database to get insights. 

Unlike data warehouses, data marts are subject-oriented and store only data belonging to a specific functional area, like the above mentioned example.

Benefits of data mart

• Access departmental data at any time: Internal teams can have quick access to data and work independently on their projects. They could meet their specific analytics requirements and access only them on time with data marts.

• Short-lived campaigns tracked in one place: be it the beta version you are testing out or any transient campaigns you run for a short time, a data mart can be used to store that data exclusively.  

• Quick to set up: Unlike data warehouses, data marts are shorter in size and easier to set up, taking only weeks to set up, and are less expensive too. Owing to the fewer data they store, they respond faster to queries too.

• Less cluttered and more manageable: since it stores only subject-related information, it can be easily managed and organized.  

How do data marts work?

Let’s assume that a company has set up a centralized data warehouse, integrating data from all departments and sources through ETL pipelines. The centralized warehouse helps decision-makers of the company analyze the company’s performance as a whole. Yet there are internal departments like sales, marketing, and product, with their own sets of metrics and analytics needs. They need filtered-down, specific insights that they can access in a real-time setup. Now, they can have data marts in two ways, either as an independent, stand-alone data mart collecting and storing data from their applications, APIs, and storage units. Or, they can have a dependent data mart that draws information from the central data warehouse.  

Anyone authorized within the department can use this data to measure and analyze their performance and make future decisions.

Types of data marts

As seen in the above section, there are three types of data marts.

• Dependent data marts

• Independent data marts

• Hybrid data marts

Dependent, as the name would have it depends on a centralized database or warehouse to receive data. It isn’t integrated directly with any applications or data sources. So, they act as subsets of another data warehouse, reflecting only what it collects from them. Data engineers use the top-down approach to create dependent data marts. 

Independent data marts work are created using a bottom-down approach. They collect data directly from external sources and applications using ETL pipelines. All these independent data marts are united and integrated into the centralized data warehouse. So, the data travels in the opposite direction here.

Hybrid is a combination of both, where it receives data from a data warehouse as well as from other data sources. Working this way, they fulfill all analytical requirements of departments.

Example of a data mart

Consider a marketing data mart. It stores data collected from website analytics, advertising platforms, and other campaign data. Marketing teams can use this data to 

• Make monthly and quarterly reports. 

• Identify and track their experiments and measure their ROI.

• Study customer behavior and design suitable initiatives.

Likewise, if there is a sales data mart, sales managers can use it to store, track, and project lead generation data. 

A data warehouse vs data lake vs data mart

A data warehouse is a centralized storage unit that connects data sources from the entire company. A data lake function serves a similar purpose too. The only difference is that the latter stores data in all formats, structures, and types. But, a data warehouse stores data suitable for analytics—storing only formatted, cleaned, and transformed data that instantly powers reporting tools. Data marts on the other hand are constructed similarly to data warehouses to store structured data but are in smaller sizes. So, you can consider marts as a subset of data warehouses.