Data Mart vs. Data Warehouse: Know the Difference

By | 2019-10-23T06:44:27+00:00 October 23rd, 2019|

For accurate business intelligence, companies rely on data warehouses. They serve as a central repository, storing existing and historical data for analyses and data-driven business decisions. But so do data marts. So, what’s the difference between these two data repositories?

In this blog, you’ll find the answer to the question data mart vs. data warehouse; what a data warehouse is and how it differs from a data mart.

Data Warehouse – An Overview

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

Figure 1. A Data Warehouse System (source: tutorialspoint)

A data warehouse refers to a structure that consolidates data from multiple source systems. The main purpose of a data warehouse is to offer correlation between data from different systems, for instance, product information stored in one system and purchase order data stored in another system.

A data warehouse is used for online analytical processing (OLAP) that involves complex querying to analyze instead of processing transactions. It’s an essential element of business intelligence as it stores large amount of data at a single location, which is then used to extract important insights and streamline business processes. Thus, it helps support the decision-making process of companies.

Data Mart – The Basics

A data mart refers to a structure that is specific to data warehousing settings. It’s a subset of a data warehouse that’s typically used to access customer-facing information. Thus, a data mart is usually focused on a business line or team.

Unlike the implementation of a data warehouse that may extend to several months or even years, a data mart is usually implemented within a few months. This is due to its smaller size (less than 100GB) and data extraction from a lesser number of sources.

A data mart is preferred for departmental analysis and reporting activities, such as sales, marketing, finance etc. as these activities are usually performed in a dedicated business unit. Therefore, enterprise-wide data is not required for BI. For example, a marketing specialist can use a dedicated data mart to perform market analysis and reporting.

Based on their requirements, companies can use several data marts for different departments and merge them to construct a single data warehouse later. This approach is called Kimball’s dimensional design method. Another method, known as Inmon’s approach, is to design a data warehouse first and then create several data marts for particular departments, as required.

Because of time and budget limitations, businesses usually opt for the Kimball approach.

Let’s take a look at how these two data repositories are different from each other.

Data Mart vs. Data Warehouse: A Comparison

The major differences between the two structures are summarized in the table below.

Data WarehouseData Mart
A data warehouse stores data from numerous subject areas.A data mart carries data related to a department, such as HR, finance, marketing, etc.
It acts as a central data repository for a company.It’s a logical subsection of a data warehouse in which the data is deposited in inexpensive servers for particular departmental applications.
A data warehouse is designed using star, snowflake, galaxy, or fact constellation schema. However, star schema is the one that is used most widely.A data mart uses a star schema for designing tables.
It’s tricky to design and use a data warehouse because it usually includes large amount of data, more than 100GB.Designing and using a data mart is comparatively easier because of its small size (less than 100GB).
A data warehouse is designed to support the decision-making process in a company. Thus, it offers an enterprise-wide understanding of a centralized system and its autonomy.A data mart is designed for particular user groups or corporate departments. Thus, it offers departmental interpretation and decentralized data storage.
A data warehouse stores detailed information in denormalized or normalized form.A data mart holds highly denormalized data, in a summarized form.
A data warehouse has large dimensions and integrates data from a large number of sources which may cause risk of failure.A data mart has smaller dimensions and data is integrated from a smaller number of sources so there’s less risk of failure.
A data warehouse is subject-oriented and time variant in which data exists for a longer duration.A data mart is intended for particular areas related to a business and it retains data for a shorter duration.

The Bottom Line

In a data warehouse, the operator is offered one integrated platform where decision support queries can be performed easily. On the other hand, a data mart offers a departmental interpretation of the stored data.

For example, a specialist from your finance department can use a financial data mart to perform fiscal reporting. However, if your business is looking forward to expanding, it requires a data warehouse because it’ll have to integrate data from a number of sources across the enterprise to make an informed decision.

The ideal data repository for an organization is the one that fits the business requirements.