• Articles
  • Tutorials
  • Interview Questions

Data Mart vs. Data Warehouse: The Major Differences

Data Mart vs. Data Warehouse: The Major Differences

Data Mart is a subset of Data Warehouse that is generally oriented for a specific purpose, i.e designed for a particular department of any organization. If the Data Warehouse contains all the data of the organization we can say, its subset Data Mart will collect only the information related to any particular section.

Following are the topics that we will be covering today to help you learn about Data Mart and Data Warehouse along with their key differences:

Table of Content

Check this video on the Data Warehouse tutorial for beginners for a better understanding of Data Warehouse Concepts.

Video Thumbnail

What is Data Mart?

What is Data Mart

A Data Mart is a subset and can perform as a valuable component of an effective data warehouse, for the needs of a specific division, or department within a company or an organization.

  • For example, a central collection will contain data for an entire business organization, while a data mart provides a specific subset of data to a specific group of users or departments so that they do not waste valuable time searching for the required data in the central collection.
  • Data marts make it easy for different departments to quickly access important data insights and help prevent departments within a business organization from interfering with each other’s data.
  • It is a filtered subsection of a data warehouse that facilitates the querying of data by specific departments or users.
  • It provides a small schema containing only the tables that belong to the groups.

Get 100% Hike!

Master Most in Demand Skills Now!

What is Data Warehouse?

What is Data Warehouse

A Data Warehouse is a storage of a large amount of operational data, that document the everyday operation of an organization, gathered from multiple sources stored under a unified schema at a single site.

  • Various operational data are analyzed and connected in the data warehouse which is collected from different sources.
  • The main concept behind data warehousing is that data stored for business can most effectively be accessed by separating it from data that are there in the operational system.
  • To execute complex analytical queries in a simple manner on large compound datasets, data warehouses are designed.
  • Data warehousing is not just the data in the data warehouse, but also the configuration and tools to collect the queries and analyze the information also.

Do you want to become a professional in the field of Data Warehousing!! we recommend you enroll in Data Warehousing Training to train yourself!!!

Data Mart vs Data Warehouse

Data Mart vs Data Warehouse

You must have gotten a clear understanding of what both data mart and data warehouse are, now a brief comparison will give you a clearer view:

                        Data Mart                   Data Warehouse
Data Mart is the subset of an organization’s data warehouse, it is related to the function or a department’s specific information.Data Warehouse contains a huge amount of information that has been collected from various sources.
It contains a limited amount of previous historical data.It contains a robust and large number of historical data.
The information in the data mart is very easy to understand and can be navigated or accessed easily.It cannot be easily navigated.
Data are kept in a data mart as a joint structure form.Data are kept in a relational database, it normalizes the data which is finally kept in the normalized structure.
It takes less time and can easily be built. It consumes more time to build because the information is kept in-detailed as the data are collected from various sources.
Data Mart can have the data or information of only one department.It contains the data or information of the whole organization.
Data Mart does not contain operational data.It consumes a huge amount of data and contains detailed operational data.
 Data Mart is a decentralized database system that uses warehouse data for a particular purpose, i.e fulfilling any needs of a department or a single user purpose.It is a centralized relational database that is capable of analyzing various sets of data from several sources.

Want to explore more about Data Warehouse? Here is a Data Warehouse Tutorial! For you, take a look at this blog by Intellipaat!

Benefits of Data Mart

There are various beneficial points related to Data Mart that you need to look at

  • Simple access to data- Less amount of data should be used by a specific organization or a particular department.
  • Fast observations- While working with a data mart, people operate a small amount of data that includes only important information and it helps to get valuable insights fast.
  • Dependability- Data Mart can perform as a valuable component of an effective Data Warehouse.
  • Decentralized system- Datamart can be set according to the needs of the manager.
  • Storage process- Stores data closer which increases performance.
  • Reduces Risk- It minimizes the risks of data usage and allows you to observe and categorize data usage which applies security and authentication to it.
  • Limited access- Data Mart is a great solution that helps limit employees’ crucial access to data or workers from different departments to any selected piece of information.
  • Low cost- Data Mart is significantly cheaper to build and maintain in comparison to a Data Warehouse.
  • Performance- Data Mart has better performance because it can solve and run all the queries at its level.

Benefits of Data Warehouse

As data resources are growing rapidly and various industries and corporations are establishing on different platforms, the need for a data warehouse becomes more vital, so you need to look into some of the important benefits of a Data Warehouse:

  • Data Warehouse stores a large amount of previously used data, which helps the users to analyze the time period and different trends to make future predictions.
  • It can be used for finding out many trends and patterns through the use of data mining.
  • As multiple sources of data are stored in the data warehouse in a single place so it allows the users to access that particular important data from multiple sources, which allows the users to save time and access the data from multiple sources.
  • With the help of other different backup resources, a data warehouse can help in recovering the resources without failures.
  • It provides the restructuring of data so that the performance of the query gets to increase and it gets easily accessible, no matter whether it’s complex analytic queries, without impacting the operational systems.
  • It is so essential to have access or control of data under the user or organization.
  • Any data which will be stored once, will remain in that exact position and in that condition only while it was stored, so the organization must verify that the data is kept securely and there is no chance of change, If any changes are to be done, it will impact the reports and analysis.

Preparing for Interviews? Here are the Top Data Warehousing Interview Questions and Answers which will guide you to crack the interview questions

Become a Database Architect

Conclusion

I hope the above-discussed topics have given you clarity regarding where to store your data and which tool is more efficient for an organization according to the requirements. If your data size is small and you don’t have much data storage expenditure, you can store it in the data mart. On the other hand, if you have enough time and money to spend on data storage you need more space to store your data and you can store it in a data warehouse.

Course Schedule

Name Date Details
SQL Training 23 Nov 2024(Sat-Sun) Weekend Batch View Details
30 Nov 2024(Sat-Sun) Weekend Batch
07 Dec 2024(Sat-Sun) Weekend Batch

About the Author

Data Engineer

As a skilled Data Engineer, Sahil excels in SQL, NoSQL databases, Business Intelligence, and database management. He has contributed immensely to projects at companies like Bajaj and Tata. With a strong expertise in data engineering, he has architected numerous solutions for data pipelines, analytics, and software integration, driving insights and innovation.