Data Mart vs Data Warehouse: 7 Critical Differences

• January 25th, 2023

Data Warehousing vs Data Mart | Hevo Data

Data platforms are a key ingredient of business success in the current era. Organizations that make better use of their data have a definite edge over their competitors. Making the most of the data is easier said than done. Implementing a data platform is a money pit and can even lead to the complete failure of the company if not done right. When it comes to the field of Data Warehouses, the decision between Data Mart vs Data Warehouse is a relatively tough one.

Data warehouses and Data marts are two confusing terms that come into any discussion about implementing a data platform. Getting to choose the right one, is about making the correct choices at a number of junctures.

A Data Warehouse acts as a large Data Storage Unit for all your business data and is used to help an organization make informed decisions. Data Marts, on the other hand, are particular subsets of Data Warehouses that work on a particular line of business.

This article provides you with a comprehensive analysis of both storage units and highlights the major differences between them to help you make the Data Mart vs Data Warehouse decision with ease. It also provides you with a brief overview of both storage units. Read along to find out how you can choose the right storage unit for your organization.

Table of Contents

What is a Data Mart?

Data Mart vs Data Warehouse: Data Mart Logo | Hevo Data
Image Source

A Data Mart is a centralized repository of information pertaining to a specific domain or subject in an organization. For example, an organization can create a Data Mart for its Finance Department or its Sales Department. It is tailor-made for a specific audience and does not contain the complete data of the organization. Data Marts usually cover only a single subject and facilitate the processing of data about that subject. 

A Data Mart is also focused and optimized for analytical tasks. It can contain data from multiple databases or sources, provided all the sources are relevant to the specific domain it addresses. Data Marts exist so that analysts are not distracted by the complete organization’s data and can quickly access the data from their domain.

What is a Data Warehouse?

Data Mart vs Data Warehouse: Data Warehouses Logo | Hevo Data
Image Source

A Data warehouse is a centralized repository of all of the organization’s data stored in a format suitable for analysis. Everything from customer data to third-party cloud-based services data can end up in a Data Warehouse. It serves as the organization’s one-stop shop where the search for any kind of data asset starts.

A Data Warehouse also facilitates the processing of all these data and serves as a foundation for all the Data Mining and Business Analysis that companies do. 

A Data Warehouse is different from a Database in the sense that it is read-focused and is optimized for analytical tasks. A typical Data Warehouse can contain data from multiple databases. A Data Warehouse is generally populated by periodic jobs that pull data from the actual data sources like databases and Cloud-Based services. At times, a Data Warehouse pulls its data from a Data Lake too.

To learn more about Data Warehouses, visit this guide- What is a Data Warehouse?

Data Mart vs Data Warehouse Comparison

Data MartData Warehouse
Data mart is project-oriented in nature.Data warehouses are data-oriented.
It is a decentralized system.It is a centralized system.
Data Mart is a bottom-up model.It is a top-down model.
It uses star schema and snowflake schema.It uses fact constellation schema.
It has a shorter life than a warehouse.It has a long life.
It is smaller in size.Data warehouses are large.
Data marts usually store data from a data warehouse.They collect data from different data sources.
It takes lesser time to process data because it handles small amounts of data.It takes longer to process data due to the large data set it handles.
Simplify Data Analysis with Hevo’s No-code Data Pipeline

Hevo Data, a No-code Data Pipeline helps to load data from any data source such as Databases, SaaS applications, Cloud Storage, SDK,s, and Streaming Services and simplifies the ETL process. It supports 100+ data sources (including 40+ free data sources) and is a 3-step process by just selecting the data source, providing valid credentials, and choosing the destination. Hevo not only loads the data onto the desired Data Warehouse/destination but also enriches the data and transforms it into an analysis-ready form without having to write a single line of code.

Get Started with Hevo for free

Its completely automated pipeline offers data to be delivered in real-time without any loss from source to destination. Its fault-tolerant and scalable architecture ensure that the data is handled in a secure, consistent manner with zero data loss and supports different forms of data. The solutions provided are consistent and work with different BI tools as well.

Check out why Hevo is the Best:

  • Secure: Hevo has a fault-tolerant architecture that ensures that the data is handled securely and consistently with zero data loss.
  • Schema Management: Hevo takes away the tedious task of schema management & automatically detects the schema of incoming data and maps it to the destination schema.
  • Minimal Learning: Hevo, with its simple and interactive UI, is effortless for new customers to work on and perform operations.
  • Hevo Is Built To Scale: As the number of sources and the volume of your data grows, Hevo scales horizontally, handling millions of records per minute with very little latency.
  • Incremental Data Load: Hevo allows the transfer of data that has been modified in real-time. This ensures efficient utilization of bandwidth on both ends.
  • Live Support: The Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
  • Live Monitoring: Hevo allows you to monitor the data flow and check where your data is at a particular point in time.
Sign up here for a 14-day Free Trial!

What is the Difference between Data Mart and Data Warehouse?

Data Warehousing vs Data Mart: Key Differences | Hevo Data
Image Source

Now that you have a basic idea of both technologies, let us attempt to answer the Data Mart vs Data Warehouse question. There is no one-size-fits-all answer here and the decision has to be taken based on the business requirements, budget, and parameters listed below. The following are the key factors that drive the Data Warehousing Data Mart comparison:

1) Data Mart vs Data Warehouse: Objective

The objective of a Data Warehouse is to act as a centralized repository of data for all business lines and departments in an organization. It is the primary search point for any data asset. It contains data about multiple objects. Typically it has raw data in a format that enables data exploration. 

A Data Mart is intended to be a repository of information pertaining to one business line or department. It can contain raw or aggregated information related to that specific domain. The sole objective behind Data Mart is to provide easy access to frequently accessed data for a specific department like Marketing, Sales, etc. 

2) Data Mart vs Data Warehouse: Data Source

The data sources for a Data Warehouse can be anything from the transactional database to a Cloud-Based service that the organization uses for conducting business. In some cases, it can also be a data lake where data from various sources is dumped in raw form.

The data source for a Data Mart depends on the way it is implemented. A dependent Data Mart is a logical subset of a Data Warehouse and hence its source is a Data Warehouse itself.

An independent Data Mart derives its information from a combination of data sources that relate to the specific domain it addresses. 

3) Data Mart vs Data Warehouse: Performance

The sole objective of creating a Data Mart is to allow easy access to relevant data for a specific department or business line. Hence, a Data Mart generally provides better performance for queries simply because it handles much less data than a Data Warehouse. 

4) Data Mart vs Data Warehouse: Data Volume

As evident from the explanations above, a Data Warehouse handles much higher data volumes since it contains all the data in an organization. The typical volume in a Data Warehouse is in TBs and in a Data Mart is in 100s of GBs.

5) Data Mart vs Data Warehouse: Data Modelling

A Data Warehouse is generally a flat structure of raw data without requiring any modeling process. A Data Mart on the other hand is usually implemented using a proper database with ACID compatibility and hence can use various modeling techniques. Star and Snowflake schema is very common in the case of Data Marts.

6) Data Mart vs Data Warehouse: Time of Implementation

Implementing and using the benefits of a Data Warehouse on-premises might take several months to fully get familiar with. If you pick a Cloud-based Data Warehouse, the entire process of configuring, familiarizing, and importing data from your sources to be analyzed further might take days to weeks.

On the other hand, if you choose to deploy an on-premise Data Mart, the time necessary to put it up might range from weeks to months, which is typically less than the time required to set up a Data Warehouse, since building or configuring on-premise Data Warehouse is difficult. Using a Cloud-based Data Mart might take anything from days to weeks.

7) Data Mart vs Data Warehouse: Cost of Implementation

In the case of an independent Data Mart, the cost of implementation is usually much lesser than a Data Warehouse. In the case of a dependent data mart, where the Data Mart is a logical subset, the cost will be higher since it needs the entire Data Warehouse architecture to be built up first.

8) Data Mart vs Data Warehouse: Types of Customers

The audience for a Data Warehouse can be anyone from Business Analysts to Senior Managers who are in charge of strategic decisions that affect the entire organization. Data Marts, on the other hand, serve employees of a particular business line or department like Marketing, Finance, etc.

That said there is nothing preventing a CEO to take a look at the Data Mart if he likes to. This difference is just a general indication of the intended audience. 

Conclusion

This article gave a comprehensive analysis of the 2 popular Database Storage Units in the market today: Data Warehouses and Data Marts. It also provides a brief overview of both Database Storage Units. It also gave the parameters to judge each of them.

Overall, the Data Mart vs Data Warehouse choice solely depends on the goal of the company and the resources it has. Data Warehouses are a good choice in almost every scene as they have a versatile and flexible nature. They can provide storage for all forms of data and help you gain valuable insights from them.

Data Marts are a good option when you need to perform analysis on a particular section of data. They also provide similar features to Data Warehouses but fine-tune your data according to the specific problem you are trying to solve. You can also read about Data Warehouse vs Data Lake here.

In case you want to integrate data from data sources into your desired Data Warehouse/destination and seamlessly visualize it in a BI tool of your choice, then Hevo Data is the right choice for you!

Visit our Website to Explore Hevo

Hevo Data with its strong integration with 100+ sources (including 40+ free sources) allows you to not only export data from your desired data sources & load it to the destination of your choice, but also transform & enrich your data to make it analysis-ready so that you can focus on your key business needs and perform insightful analysis using BI tools.

Want to take Hevo for a spin? Sign Up for a 14-day free trial and experience the feature-rich Hevo suite firsthand.

Share your experience of learning about Data Mart vs Data Warehouse, in the comments section below.

No-code Data Pipeline For your Data Warehouse