In the modern day, data platforms are an essential component of business success. Organizations that make greater use of their data have a significant edge over their competition. Making use of data is easier said than done. Also, implementing a data platform is a money hole that, if not done correctly, may lead to the company's utter demise. When it comes to data warehouses, deciding between data warehouse and data mart is a difficult task.

Data warehouses and data marts are two ambiguous words that frequently appear in discussions about creating a data platform. Choosing the right one entails making the proper decisions at various points.

A data warehouse serves as a big data storage unit for all of your company data, and it is utilized to assist an organization in making educated decisions. On the other hand, data marts, are subsets of data warehouses that focus on a certain line of business.

This informative blog gives a complete analysis of both storage units and shows the key distinctions between them to help you make the data warehouse vs data mart discussion with ease. It also gives you a quick overview of both storage containers. Continue reading to learn how to select the best storage unit for your business.

What Is a Data Warehouse?

Well, a data warehouse is a consolidated repository of all of the organization's data kept in a manner appropriate for analysis. A data warehouse may include anything from client data to data from third-party cloud-based applications. It acts as the organization's one-stop shop for searching for any type of data asset.

A data warehouse also makes it easier to analyze all of this data and acts as the foundation for all of the business analysis and data mining that firms perform.

A data warehouse differs from a database in that it is read-focused and designed for analytical operations. A typical data warehouse might contain information from several databases. Periodic operations that pull data from actual data sources such as databases and cloud-based applications build a data warehouse. A data warehouse may also pull data from a data lake at times.

What Is a Data Mart?

To put it simply, a data mart is a consolidated store of information relevant to a given topic or subject in an organization. For instance, a corporation can construct a data mart for its sales department and finance department. It is tailored to a certain audience and does not include all of the organization's data. Data marts often cover a specific subject and aid in the processing of data related to that subject.

A data mart is also specialized and tailored for analytical purposes. It can contain data from numerous databases or sources, as long as all of them are relevant to the domain it targets. Data marts exist to keep analysts from being distracted by the entire organization's data and to allow them to easily access data from their domain.

Data Warehouse vs Data Mart: A Comparative Analysis

Now that you have a fundamental grasp of both technologies, let's attempt to solve the difference between data warehouse and data mart. There's no one-size-fits-all solution here and the decision has to be determined depending on the company's requirements, parameters, and the budget listed below. The primary elements driving the data warehouse and data mart comparison are as follows:


Data Warehouse

Data Mart


It works as a central data store for a firm.

It's a logical subset of a data warehouse wherein data is stored on low-cost servers for specific departmental applications.


It assists in making a strategic decision for a firm.

It assists in making tactical decisions for the entire business.


It's data-oriented.

It's project-oriented.


To serve as a consolidated store of data for every business line and department within a company.

It is a store of information or data for a single business line or department.


It is designed utilizing a snowflake, star, fact constellation, or galaxy structure.

It employs a star schema when it comes to designing tables.


It is often a flat structure of raw data that does not need any modelling procedure.

It is generally created using a good database with ACID compliance and consequently may employ numerous modeling strategies.

Data Handling

Because of the enormous amount of data, it takes quite a long time in order to process data.

Comparatively takes less time since it just handles a very little amount of data.


To store all company-related data.

Holds data that is specialized to a single department.


It is leveraged to store information or data for many subject areas.

It stores information on a certain department, like marketing, finance, or HR.

Data Storing

Exclusively designed to keep track of enterprise-wise decision data.

Star schema design and dimensional modeling are used to improve access performance.

Data Type

It stores data in its most complete form.

It contains data in a summarized manner.

Data Value


Numerous lines-of-business

A multi-functional department or a single lines-of-business


It might be anything from the cloud-powered service to the transactional database that the firm employs for conducting business.

It relies on the way it has been implemented. Because dependent data is basically a logical subset of a data warehouse, its source is also a data warehouse.


It is more than 100 GB in size.

It is also more than 100 GB in size.


The cost of implementing an independent data mart is often substantially lower than that of a data warehouse. 

The cost of a dependent data mart, where the data mart is a logical subset, will be greater since the full data warehouse architecture must first be created.


It's a bit challenging due to the vast storage and intricate data.

Maintenance is quite simple when compared to data warehouses .

Implementation Time

Implementing and utilizing the benefits of an on-premises data warehouse may take many months to become completely acquainted. If you choose a Cloud-powered data warehouse, the process of familiarizing, importing, and setting data from your sources to be further analyzed might take days to weeks.

If you wish to deploy an on-premise data mart, the time required to set it up can range from weeks to months, which is usually less than the time needed to set up a data warehouse because establishing or configuring an on-premise data warehouse is challenging. Leveraging a cloud-powered data mart may take anything from a few days to many weeks.

Key Differences Between Data Warehouses and Data Mart

The following are the key differences between data warehouses and data mart:

  • Data warehouse keeps a significant quantity of data acquired from many sources, whereas a data mart simply includes the precise data from the data warehouse required by the organization for analysis.
  • Data warehouse contains data from all departments in a business, whereas data mart only contains data from certain departments.
  • Data warehouse includes a lot of data and owing to the vast volume of data, conducting data operations on it is quite time intensive. Data marts, on the other hand, contain less data and so data operations in data marts require less time.
  • Data warehouse planning procedure is tough because of many types of complex data but the data mart designing process is uncomplicated.
  • data warehouses typically have storage ranging from 100 GB to 1 TB+, whereas data marts have less than 100 GB.
  • A data warehouse deployment often takes a month to a year, but a data mart implementation typically takes a few months. 
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This article offered a complete review of the 2 prominent Database Storage Units on the market today: Data Warehouses and Data Marts. It also gives a quick rundown of both Database Storage Units. It also provided the criteria for judging each of them.

Overall, the data warehouse vs data mart option totally depends on the purpose of the firm and the resources it possesses. Because of their versatility and flexibility, data warehouses are an excellent solution in nearly every situation. They can supply storage for various sorts of data and help you acquire important insights from them.

Data marts are a wonderful alternative when you need to do an analysis of a certain segment of data. They provide comparable functionality to data warehouses , but they tailor your data to the precise problem you're attempting to address.

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Frequently Asked Questions

1. What are the 2 advantages of data mart compared to data warehouse?

Listed below are two of the most common advantages that a data mart has over a data warehouse:

  • Access to a data warehouse will be restricted. In contrast, a data mart may be created and managed by a single department.
  • Because data marts include information about a given subject or department, finding and retrieving relevant data is easy, saving time and resources.

2. What is a data mart in data warehousing?

A data mart is generally a subset of a data warehouse concentrating on a specific line of business, subject area, or department. Data marts make specialized data available to a designated group of users, allowing them to rapidly obtain crucial insights without having to sift through a whole data warehouse.

3. What are the three main types of data warehouses ?

The three main types of data warehouses are EDW (enterprise data warehouse), ODS (operational data store), and data mart.

4. Can you have a data mart without a data warehouse?

An independent data mart is a self-contained system constructed without the assistance of a data warehouse that concentrates on a single subject or business function.

5. Is Snowflake a data mart?

Snowflake is basically the data warehouse that may replace data marts.

6. What are the 4 key components of a data warehouse?

Typically, a data warehouse consists of four major components:

  • ETL (extract, transform, load) tools
  • A central database
  • Access tools
  • Metadata

7. What is the difference between OLAP and OLTP?

The two words appear to be synonymous, however, they relate to different types of systems. In real-time, OLTP (online transaction processing) gathers, saves, and processes data from transactions. Complex queries are used in PLAP (online analytical processing) to evaluate aggregated historical data from OLTP systems.

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