Difference Between Data Warehouse and Data Mart

Business has been changed by cloud computing, which makes it possible for organizations to swiftly and securely access information about their customers, workers, and goods. To make key business choices, this data is used. Large volumes of data can be stored in either a Data Mart or a Data Warehouse. However, their usefulness varies. This article discusses the fundamental distinctions between a data warehouse and a data mart in order to help you make an informed choice about how to manage your data.

Cloud-based technology has revolutionized the business world, allowing companies to quickly retrieve and store valuable data about their customers, employees, and products.  This information is utilized to make critical business decisions. Both Data Mart and Data Warehouse are popular terms for storing large amounts of data. However, they differ in terms of usefulness.

Understanding how data warehousing and data marts help firms is critical to stay competitive in a rapidly changing IT world. This article highlights the key differences between data warehouse and data mart to assist you in making an informed decision about how to handle your data. But before we compare Data Warehouse vs Data Mart, let's define what each of these terms means.

Data Warehouse vs Data Mart: Table Of Content

What is a Data Warehouse?

A data warehouse is a centralized collection of data that can be studied to help businesses make better decisions. Data flows into a data warehouse regularly from transactional systems, relational databases, and other sources. Business analysts, data engineers, data scientists, and decision-makers use BI tools, SQL clients, and other analytics software to access the data.

For organizations to remain competitive, data and analytics have become essential. Business users depend on dashboards, reports, and analytics tools to extract data insights, monitor business performance, and support decision-making.

How does Data Warehouse work?

Multiple databases can be found in a data warehouse. Data is structured into tables and columns within each database. You can define the collected data within each column, such as integer, data field, or string. Tables can be arranged within schemas, which are similar to folders. When data is ingested, it is stored in the schema's numerous tables. The schema is used by query tools to decide which data tables to access and examine.

If you would like to become a Data Warehouse certified professional, AWS Data Warehousing Training Course. This course will help you to achieve excellence in this domain.

Benefits of Data Warehouse

The following are some of the advantages of a data warehouse:

  • Provides improved business intelligence
  • Data from a variety of sources has been compiled.
  • Analyzing historical data
  • Improves consistency, quality, and accuracy of data
  • Separation of analytics and transactional databases, which increases both systems' performance.

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Data Warehouse Use Cases

  • To make an informed decision, a company considering expansion must use data from a range of sources within the organization. This necessitates the creation of a data warehouse that collects information from sales, store management, customer loyalty, supply chains, and other sources.
  • A variety of things influences an insurance company’s profitability. A centralized data warehouse is needed by an insurance firm reporting on profits to combine data from its claims department, sales, client demographics, investments, and other areas.
Related Article: Data Warehouse vs Data Lake

What is a Data Mart?

A data mart is a portion of a data warehouse that is dedicated to a specific business function. It divides the entire dataset into manageable, relevant bits, such as information from a company's finance or marketing departments.

Every day, modern organizations collect a massive amount of data, both structured and unstructured. Running searches against the entire dataset can be time-consuming due to the volume of data. In most cases, end-users would have to create sophisticated queries merely to get relevant data that could then be examined. Data marts enable considerably faster access to important information by segmenting data into business roles. As a result, they speed up the data retrieval procedure. 

What Is Data Mart

There are three different types of data marts, each with its own relationship to the data warehouse and its own set of data sources.

  • Dependent Data Marts

Within an enterprise data warehouse, dependent data marts are partitioned parts. The storage of all enterprise data in one central location is the first step in this top-down approach. When a subset of the primary data is needed for analysis, the newly generated data marts extract it.

  • Independent Data Marts

Independent data marts are self-contained systems that do not require the utilization of a data warehouse. Data about a particular subject or business process can be extracted from internal or external data sources, processed, and then stored in a data mart repository until the team needs it.

  • Hybrid Data Marts

Data from existing data warehouses and other operational sources are combined in hybrid data marts. This unified strategy combines the top-down technique's speed and user-friendly interface with the independent method's enterprise-level integration.

Related Article: List of Top Data Warehouse Tools

Benefits of Data Mart

A data mart provides various benefits to the end-user due to its smaller, more robust design:

  • Simplified data access
  • Quicker access to insights
  • Simpler data maintenance
  • Easier and faster implementation
  • It's simple to integrate with business intelligence tools.
  • Gives you a clearer picture of data for each line of business.
  • Better performance because queries may be done at the data mart level.
  • Can data be organized in a way that makes it more accessible to business-line
  • Departments have complete control over their data workloads.
  • Data marts can also serve as a foundation for a more powerful data warehouse.

Data Mart Use Cases

  • A data mart method is preferred because marketing research and reporting are often handled in a specialized business unit and do not require enterprise-wide data.
  • A financial analyst can use a finance data mart to provide financial reporting.
Related Article: Data Warehouse Interview Questions

Data Warehouse Vs. Data Mart: What are the differences?

The key differences between data marts and data warehouses that you should be aware of.

ParameterData WarehouseData Mart
DescriptionA data warehouse is a sort of data management system intended to facilitate and assist business intelligence (BI) and analytics activities. Data warehouses are designed mainly for querying and analysis, and they frequently store vast amounts of historical data. Data warehouses are designed to access large groups of related records.A data mart is a structure/access pattern used to retrieve customer data in data warehouse setups. A data mart is a subset of a data warehouse typically focused on a single business line or team.
UsageEnterprise-wide analysis of disparate data sourcesA single subject or enterprise’s Department-specific area
Data Sources Many external and internal sources are from various areas of an organization.Only a few sources are tied to a single line of business.
Size A data warehouse is usually larger than 100 GB and generally a terabyte or moreA data mart generally is less than 100 GB
Range Typically enterprise-wide and ranges across multiple areasLimited to a single focus for one line of business.

The process of designing a Data Warehouse is pretty challenging.  It May or may not be possible to use it in a dimensional model. It can, however, feed dimensional models.

A data warehouse is a top-down model.

The Data Mart design procedure is simple.  It's designed around a dimensional model with a star schema.

While it is a bottom-up model

Data ProcessingData warehousing affects a big portion of the company, which is why it takes so long to process. Because they can only handle small amounts of data, data marts are simple to use, create, and install.
FocusAll departments are concerned with data warehousing. It may represent the entire organization.Data Mart is a department-level tool that is subject-oriented.

Data warehousing is more valuable because it can get data from any department.

A data mart is a collection of data from a certain department within a firm. Sales, finance, marketing, and other departments may have their data marts. It has restricted applicability.
SizeThe Data Warehouse might be anywhere from 100 GB to 1 TB+ in size. Data Mart is less than 100 GB in size.
Time to implementThe time it takes to implement a Data Warehouse might range from months to years.

The Data Mart implementation process is only a few months long.

Data heldComplete detailed dataTypically summarized data
CostIt varies but is frequently greater than $100,000; however, cloud solutions can be far less expensive because corporations pay per use.Typically, the price ranges from $10,000 to $50,000.


A single community or department.


With this, we have come to the end of this article, “Data Warehouse vs. Data Mart.” We hope that the differences stated above will assist you in determining which option is best for your needs and will help your organization develop.

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Last updated: 03 Apr 2023
About Author


Madhuri is a Senior Content Creator at MindMajix. She has written about a range of different topics on various technologies, which include, Splunk, Tensorflow, Selenium, and CEH. She spends most of her time researching on technology, and startups. Connect with her via LinkedIn and Twitter .

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