Difference Between Data Warehouse and Data Mart

Data Warehouse and Data MartData warehouse and data mart are the data collecting bodies but they differ from each other in several ways. The data warehouse collects data from several organization sources and provides meaningful business insight. While we can consider data mart as a subtype of a data warehouse that focuses on a specific subject and helps to draw meaningful department insight.

Many people get confused with these terms and even some authors use both of these terms synonymously. Most of the time IT managers of the organization are confused about whether they should design a data warehouse first or a data mart first. To clear this confusion, one must ask the following fundamental questions:

  • Top-down or bottom-up approach?
  • Enterprise-wide or department-wide?
  • What to design the first data warehouse or data mart?
  • Dependent or independent data marts?
  • Whether to pilot build or implement full fledge?

So, let’s get a quick comparison between these two kinds of databases with the help of a comparison chart.

Data Warehouse Vs Data Mart

  1. Quick Comparison
  2. What is Data Warehouse?
  3. Types of Data Warehouse
  4. What is Data Mart
  5. Types of Data Mart
  6. Summary

Quick Comparison: Data Warehouse Vs Data Mart

BasisData WarehouseData Mart
DefinitionA data warehouse is a huge storage of data collected from various departments of the organization.  A Data mart is a subset of a data warehouse targeted to a single subject created to meet the need of a group of users of the organization.
CentralizationData Warehouse is Centralized and broadly focuses on all departments of the organizationData Mart is Decentralized
Decision MakingHelps in making the strategic decisionHelps in making a tactical decision
DesigningDesigning a data warehouse is a difficult task Designing a data mart is an easy task
UsageBusiness wide analysisDepartment-specific analyses
ProcessingIt takes a long time to process data from a data warehouse as it contains a huge amount of dataIt takes less time to process data from a data mart
LifeThe data warehouse has a long lifeData mart has a short life 
Implementation timeImplementation can take several months to a few yearsThe implementation only takes a few months; it is not extended more than that
SizeThe data warehouse has a vast size it can be from 100 GB up to 1 TBData mart is comparatively smaller in size and it can range up to 100 or less than that

What is Data Warehouse?

A data warehouse aggregates the data from various sources into a single, central data repository. The data warehouse supports techniques such as data mining, machine learning, and artificial intelligence. It also enables data analysts to analyze a huge amount of historical data of the organization to provide meaningful business insight.

The data warehouse has been providing the business solution for over three decades. Conventionally data for the data warehouse was drawn on the platform of the mainframe computer. It uses to collect data from various sources, clean it, load, and maintain data in a relational database.

Data in a data warehouse can in be different forms. It can be:

  • Structured
  • Semistructured
  • Unstructured

The data is first processed, transformed, and then stored in the data warehouse. To access and analyze data in the data warehouse we use business intelligence tools, query tools, SQL clients, etc.

In this way merging all information in one place i.e., in a data warehouse, organizations ensure that they consider all information to analyze their customer more holistically.

Types of Data Warehouse

We can categorize data warehouses into three types discussed below:

Enterprise Data Warehouse

The entire data of an enterprise is centrally stored in a data warehouse that we refer to as an enterprise data warehouse. In an enterprise data warehouse, the data is organized and represented in an integrated way that enables the enterprise to classify and access data according to a specific subject.

Operational Data Store

An operational data store or ODS is a data storage that serves as an alternative that supports the organization’s reporting needs. ODS refreshes the data in the data warehouse in real-time. Thus it is usually preferred over the conventional data warehouse to record the routine activities of the employees.

Data Mart

A Data mart is a subset of the data warehouses that specifically focus on a particular subject such as sales or finance.

What is Data Mart?

A Data mart is a logical subset of the data warehouse. It particularly focuses on a specific business, department, or subject. The database drawn by the data mart is made available to a specific group of users. It helps them quickly access the business insight without wasting their time in the entire data present in the data warehouse.

The data in the data mart is somehow related to the data warehouse. So, if we categorize data marts based on their relationship with the data warehouse then there are three types of data marts.

Types of Data Mart

Dependent Data Marts

Data for these kinds of data marts is drawn from the organization’s data warehouse. It extracts a defined subset of data from the data warehouse whenever it is required for analysis.

Independents Data Marts

Data for the independent is not drawn from the data warehouse of the organization. Its data is extracted on a particular subject from internal or external data sources. The data is then processed and stored in it and made available to analysts whenever required.

Hybrid Data Marts

The data for the hybrid data mart is drawn from both organization’s data warehouse and from other operational sources.


Data warehouse and data mart differ in the way that data in the data warehouse is collected from various sources of the organization in centralized storage. However, data in a data mart is collected to meet the specific need of the group of users of the organization.

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