Databases are an essential component of any organization’s toolset. However, only data managers are usually aware of the variations between the various database types. Hence, for non-experts, we describe the fundamental differences between a data warehouse vs data mart in this article.
What is a Data Warehouse?
To give valuable business insights, a Data Warehouse collects and organizes data from various sources. It is a collection of data that is separate from the company’s operational systems and aids in decision-making. Data is kept in a Data Warehouse from a historical perspective.
The data in the warehouse comes from a variety of functional units. It is then examined, sanitized, and integrated into the data warehouse system. A fast computer system with a huge storage capacity was used in the data warehouse. This tool can answer any sophisticated data-related queries.
What is a Data Mart?
A data mart is a straightforward Data Warehouse. It concentrates on a specific topic. Hence, only a few sources are used by Data Mart. These sources for instance could be a central data warehouse, internal operational systems, or external data.
A Data Mart is also a system that indexes and extracts information. Therefore, it is a crucial part of a data warehouse. For example, it is subject-oriented and tailored to the requirements of a certain user group. Data marts are faster and easier to use than data warehouses because they only employ modest amounts of data.
Data Warehouse vs Data Mart: Major Differences
The size and strategy of the two databases are the most significant differences. A data mart holds a small amount of data linked to a specific business department or project, whereas a data warehouse acts as the organization’s global database and saves data concerning any area of the company. A data warehouse, similarly, collects data from a number of sources, but a data mart often collects data from the data warehouse’s core database. As a result, a data warehouse has a significantly bigger storage capacity and a far more sophisticated and difficult to design architecture than a data mart.
Furthermore, a data warehouse’s implementation process is more complex and time-consuming as it typically takes several months or even years. Whereas that of a data mart may be completed in a matter of months because it collects considerably less data and has a simpler structure.
Other differences include:
Parameter | Data Warehouse | Data Mart |
Objective | The basic goal of a Data Warehouse is to deliver a unified environment and a consistent view of the business at any given time. | A data mart is primarily used at the department level in a business division. |
Design | The Data Warehouse design process is pretty tough. | The Data Mart design procedure is simple. |
Usage | It aids in strategic decision-making. | It assists in making tactical business decisions. |
Data Handling | Data warehousing affects a big portion of the company, which is why it takes so long to process. | Because they can only handle tiny amounts of data, data marts are simple to use, create, and install. |
Focus | All departments are concerned with data warehousing. It’s feasible that it will represent the entire organization. | Data Mart is used at the department level and is subject-oriented. |
Data Type | When opposed to data mart, the data kept in the Data Warehouse is always detailed. | Data Marts are designed for certain user groups. As a result, data is restricted. |
Subject Area | The basic goal of a Data Warehouse is to deliver a unified environment and a consistent view of the business at any given time. | Data Marts often contain only one subject area, such as sales figures. |
Data Storing | Not only for marketing data, but for all enterprise-wide decision data. | The performance of the access layer was optimized using dimensional modeling and star schema design. |
Data Value | From the perspective of the end-users, read-only. | Regardless of grain, transaction data is provided straight from the Data Warehouse. |
Scope | Data warehousing is more beneficial 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 own data marts. Has a restricted application |
Source | Data is collected from a variety of sources in a data warehouse. | Data in Data Mart comes from a limited number of sources. |
Implementation Time | The Data Warehouse deployment process can take months or even years. | The Data Mart implementation process is only a few months long. |
Size | The Data Warehouse might be anywhere from 100 GB to 1 TB+ in size. | Data Mart is less than 100 GB in size. |
In conclusion:
A data warehouse, in simple terms, is a central database with the capacity to connect to nearly any data source and vast storage capabilities. A data mart, on the other hand, is a subsection of a data warehouse with limited storage capacity and is designed to answer data consumers’ questions about a certain business sector.
In other words, finding someone who does not understand a database is tough. Similarly, Databases are now to business what digital whiteboards would be to education if they had totally replaced traditional chalkboards. In short, Only technicians, data analysts, and data scientists are likely to understand the variations between the many types of databases and their uses inside an organization.