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The Difference Between Data Warehouses and Data Marts

Published on 19 January 18
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The difference between the data warehouse and data mart can be confusing because the two terms are sometimes used incorrectly as synonyms. Here is the basic difference between data warehouses and data marts.

Data Mart

Generally, a data mart can be thought of as a subset of a data warehouse. The data mart is a storehouse of data that is meant to serve a specific community and is designed to meet the needs of a specific group of users.

The Difference Between Data Warehouses and Data Marts - Image 1

Because data marts are optimized to look at data in a unique way, the design process tends to start with an analysis of user needs. Data marts are usually controlled by a single department of an organization like sales, finance, etc. The data for these data marts is assembled only from a few sources. Therefore, data marts and data warehouses mainly differ in their scope and data sources.

Data Mart Features

  • A data mart holds only one subject area, i.e. finance or sales

  • A data mart focuses on integrating information from a given subject area or set of source systems.

  • A data mart is built focused on a dimensional model using a star schema.

Data Warehouse

A data warehouse is a big central repository for all of an organization's historical data. It is stored from a historical perspective. This data is assembled from different departments and units of the company.

The Difference Between Data Warehouses and Data Marts - Image 2

Fundamentally, a data warehouse is a collection of data that is separated from the operational systems. It helps in the decision-making of the company.

The data warehouse's design process tends to start with an analysis of what data already exists and how it can be collected and managed in such a way that it can be used later on.

The size of a data warehouse is typically larger than 100 GB, whereas data marts are generally less than 100GB. Due to the difference in scope, it is comparatively easier to design and use data marts.

Data virtualization software can be used to create virtual data marts, extracting data from different sources and merging it with other data as necessary to meet the needs of specific business users.

Data Warehouse Features

  • A data warehouse holds multiple subject areas.

  • A data warehouse works to integrate all data sources.

  • A data warehouse holds very detailed information.

  • A data warehouse doesn't necessarily use a dimensional model but does feed dimensional models.

Here, I explained basic differences between data warehouses and data marts. Leave your insights about this article in the comments.

The difference between the data warehouse and data mart can be confusing because the two terms are sometimes used incorrectly as synonyms. Here is the basic difference between data warehouses and data marts.

Data Mart

Generally, a data mart can be thought of as a subset of a data warehouse. The data mart is a storehouse of data that is meant to serve a specific community and is designed to meet the needs of a specific group of users.

The Difference Between Data Warehouses and Data Marts - Image 1

Because data marts are optimized to look at data in a unique way, the design process tends to start with an analysis of user needs. Data marts are usually controlled by a single department of an organization like sales, finance, etc. The data for these data marts is assembled only from a few sources. Therefore, data marts and data warehouses mainly differ in their scope and data sources.

Data Mart Features

  • A data mart holds only one subject area, i.e. finance or sales

  • A data mart focuses on integrating information from a given subject area or set of source systems.

  • A data mart is built focused on a dimensional model using a star schema.

Data Warehouse

A data warehouse is a big central repository for all of an organization's historical data. It is stored from a historical perspective. This data is assembled from different departments and units of the company.

The Difference Between Data Warehouses and Data Marts - Image 2

Fundamentally, a data warehouse is a collection of data that is separated from the operational systems. It helps in the decision-making of the company.

The data warehouse's design process tends to start with an analysis of what data already exists and how it can be collected and managed in such a way that it can be used later on.

The size of a data warehouse is typically larger than 100 GB, whereas data marts are generally less than 100GB. Due to the difference in scope, it is comparatively easier to design and use data marts.

Data virtualization software can be used to create virtual data marts, extracting data from different sources and merging it with other data as necessary to meet the needs of specific business users.

Data Warehouse Features

  • A data warehouse holds multiple subject areas.

  • A data warehouse works to integrate all data sources.

  • A data warehouse holds very detailed information.

  • A data warehouse doesn't necessarily use a dimensional model but does feed dimensional models.

Here, I explained basic differences between data warehouses and data marts. Leave your insights about this article in the comments.

This blog is listed under Development & Implementations and Data & Information Management Community

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