Difference Between Data Warehouses and Data Marts

This article will provide a clear comparison between Data warehouses and Data Marts. Before comparing them first let us understand what are data warehouses and data marts.

What is a Data Warehouse?

A data warehouse is a central location where a company can store vast amounts of data gathered from numerous sources. It is intended to help business intelligence (BI) activities by enabling users to examine the data and come to wise conclusions.

A data warehouse's main objective is to offer a unified picture of data from many systems and databases. Data is organized, cleaned up, and turned into a format that is best for reporting and analysis in this unified and structured storage area. The extract, transform, and load (ETL) procedure is used for achieving this.

Data warehouses are often designed using a combination of hardware, software, and database systems capable of handling huge amounts of data and complicated queries. They use strategies including indexing, partitioning, and data compression to boost storage effectiveness and performance.

The capability of a data warehouse to allow the storing of historical data is one of its important features. It collects and saves information over time, allowing users to analyse trends, measure performance, and compare historical patterns. Making decisions and developing a strategy are made much easier with this.

Types of Data Warehouses

There are different types of data warehouses that are classified based on their purpose and usage. A few of them are mentioned and explained below

Enterprise Data Warehouse (EDW):This is the most well-known and traditional type of data warehouse. An EDW collects data from multiple sources across an organization and stores it in a centralized location. It acts as the organization's complete and uniform source of truth for data. EDWs are appropriate for large-scale businesses with complicated data requirements since they are often created utilizing a top-down methodology.
Operational Data Store(ODS):The functionality and purpose of ODS are quite different from EDW. It concentrates on integrating operational systems within an organization's systems in real time. Compared to EDWs, ODSs are made to support operational procedures and offer a more up-to-date view of the data. They frequently serve as a holding space for data before it is further converted and fed into the data warehouse.
Data Mart:The smaller, more concentrated portion of a data warehouse is called a data mart. It is adapted to the requirements of a specific division, group, or business function and contains specific data. Usually, the purpose of data marts is to give specialized users or departments quick, targeted access to information so they may do specialized analysis and reporting tasks. Data marts come in two varieties: dependent (derived from the EDW) and independent (stand-alone).

What is Data Mart?

As mentioned earlier Data Mart is a condensed or narrowed portion of a complete data warehouse. It is a scaled-down, more targeted version of a data warehouse that includes information essential to a certain user group.

Data marts are created to specifically address the analytical and reporting requirements of a given department or team. They often keep data that has been pre-aggregated, processed, and organized to meet the users' particular needs. Data marts can be independent, stand-alone storage facilities or they can be formed utilizing data from the central enterprise data warehouse (EDW).

A data mart's primary goal is to give users who need to execute certain analytical or reporting jobs quick and simple access to relevant information. In contrast to an EDW's comprehensive and enterprise-wide nature, data marts give a more streamlined and simplified view of data by concentrating on a particular department or business function.

With a bottom-up design strategy, data marts are frequently created by first identifying specific business needs, then developing the data mart to meet those needs. They can be organized in a way that is in line with the demands of the team or department, such as grouping data by the particular dimensions or measurements that matter most to their study.

The significant benefit of data marts is that they are very easy to design and implement. They offer very faster data access and users can run analyses and acquire insights related to their particular area because of its streamlined and simplified view of the data.

Difference between Data warehouses and Data Marts

S.No.	Data warehouse	Data Marks
1.	Data repository that is comprehensive and centralized for the entire organization.	A division or portion of a data warehouse that focuses on a particular division or business activity.
2.	collects information from multiple sources within the organization.	extracted from a data warehouse or separate sources.
3.	designed to meet the needs of the entire company in terms of reporting and analysis	designed to fulfil the specific analytical requirements of a certain department or team.
4.	manages large amounts of data from numerous sources.	Contains fewer data points relevant to a certain user group.
5.	The top-down approach is used for developing and implementing it at the corporate level.	The bottom-up approach is used to implement data marks for fulfilling the specific business requirements.
6.	Allows for the storing and study of historical data.	Mainly focuses on more recent or near real-time data.
7.	Significant resources and infrastructure are required to administer and sustain.	Can be designed and executed fast with limited resources.
8.	Provides a unified view of data across the organization.	Provides a streamlined and simplified representation of data that is suited to individual user requirements.
9.	Serves as the organization's primary source of truth for data.	Provides a larger department-specific or function-specific view of data.
10.	Supports complicated and in-depth analysis, including multidimensional analysis (OLAP).	Typically, it gives simplified analytical capabilities for certain use scenarios.