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.
What is a Data Mart?
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?
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 Mart | Data 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. |
What is the Difference between Data Mart and Data Warehouse?
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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) 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 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) 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 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 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) 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) 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) 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.
Curious about Data Lake vs Delta Lake? Check out our detailed guide to learn the key distinctions and determine which option suits your data strategy.
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 .
Share your experience of learning, in the comments section below.
Talha is a Software Developer with over eight years of experience in the field. He is currently driving advancements in data integration at Hevo Data, where he has been instrumental in shaping a cutting-edge data integration platform for the past four years. Prior to this, he spent 4 years at Flipkart, where he played a key role in projects related to their data integration capabilities. Talha loves to explain complex information related to data engineering to his peers through writing. He has written many blogs related to data integration, data management aspects, and key challenges data practitioners face.
