Data mart vs data warehouse: Comparison

Differences between data warehouses and data marts

At the outset, both the data warehouse and data mart are storage solutions that store data in a structured format. But their purposes vary widely. 

A data warehouse is more of a centralized repository that stores organization-wide data collected from different sources and structured in a similar format.

Data marts are a subset of data warehouses and are for a specific storage purpose. For example, storage of data of a specific department like finance, marketing, etc.

Why do you need different methods to store data then? This is because different applications have different storage needs. With data flowing from all directions and companies wanting to have a more granular view, it’s better to have an organized approach and choose the appropriate storage method. 

Here are the basic differences between data marts and data warehouses.

Inmon vs Kimball approach 

The Inmon vs Kimball approach has been long debated. Both of them are about how data warehouses and marts are connected in the data architecture design. One is proposed by Bill Inmon, according to him transactional data from different sources should be loaded directly into a data warehouse after the ETL process, and data marts should be dependent on them to receive a specific part of the data. 

This is exactly opposite to what Ralph Kimball proposes. He suggests building data marts first to connect OLTP sources, and these data marts are connected to a centralized data warehouse to store organization-wide data using star or snowflake dimensional models. 

Each method has its advantages and drawbacks. For example, Inmon’s approach is easier to maintain and requires low development costs, but it’s not suitable for unstable data sources and reporting needs. Also, it requires higher cost to set up compared to the Kimball method.

Kimball’s method is faster to build and requires low cost as it’s built iteratively. 

So, you cannot just decide one is ideal over the other and it’s totally up to the organization and its data and reporting process. Also, due to the 

For example, if the organization and leadership demand centralized reporting, then Inmon’s approach works better. Or, if your organization gives importance to individual functional units and their metrics, then follow Kimball’s process.

Refer to the following table to see how both differ from each other.

Types of data marts

There are three types of data marts based on their functionality, dependent, independent, and hybrid data marts.

Dependent data marts

As the name suggests, these data marts are dependent on a centralized data warehouse and are built as a part of them. These marts are built using the top-down approach and they are placed close to the end users’ reporting environment. Data flow here this way - data sources - data warehouses - data marts. With the help of ETL processes, data is fetched and loaded into data warehouses. From here, department-specific data marts can receive relevant data and display them. Without the data warehouse, data marts cannot function separately on their own.

Independent data marts

This kind of data mart isn’t dependent on any data warehouse. Rather they collect data directly from one or multiple data sources. This is best suited for small companies where each department has a certain amount of applications, but in smaller amounts and wants to run their analytics. In such cases, an independent data mart is more suitable than a data warehouse.

However independent data marts can be against the idea of having a central source of truth for the whole organization that reflects every data source. 

Hybrid data marts

This is a combination of dependent and independent data marts. They depend on a data warehouse to receive data and also on other data sources. This perfectly fits the changing analytics requirements of growing organizations as they can flexibly see what they want to measure by connecting the relevant data sources. 

For example, your marketing team is testing out a new tool and wants to measure the effectiveness of the campaign separately. This data point can be connected to their data mart directly which also receives specific data from a central warehouse. Later on, the data source can be either connected to the warehouse directly or removed altogether.

What is data mart?

As mentioned earlier, data marts are subsets of data warehouses. They are meant for smaller smaller to considerable amounts of data similar to a data warehouse. Due to the spacing constraints, they can be used to store data that represents a specific part of a business.

Many modern businesses are using data marts to empower their teams separately to have a unified view of their data.

This helps the departmental leaders to not go through the entire enterprise data to view their critical insights.

Similar to a data warehouse, a data mart stores structured data in the form of tables like fact tables and dimension tables using star, snowflake, or vault schema. 

What is a data warehouse?

A data warehouse is a centralized repository to store company-wide data. Unlike data marts, data warehouses can store large amounts of data. It stores data in a structured format which is drawn from multiple data sources, cloud applications, flat files, other databases, etc. This data can be used to power the business intelligence needs of the company from time to time. 

A data warehouse architecture hosts more components than just the storage part - the ETL process to load data into the warehouse, the visualization component to help with decision-making and insights analysis, and other data analytics and science applications that produce instant, complicated inferences.

Traditionally, data warehouses were built within on-premise locations. However, the advent of the cloud has made it much simpler to set up a cloud or hybrid-based warehouse and safely store data while enjoying high performance and scalability.

Real-time examples for data marts

Here is how businesses get benefits through data mart architecture.

Marketing and sales analysis

Unlike other teams, the marketing and sales teams have higher analytical needs and struggle with different facets of data from different applications.

They can use data marts to organize their data in one place and generate custom reports to understand their metrics.

Budget and financial analysis

Budget and expense analysis is important to track to achieve your targeted revenue goals. This involves combining and analyzing different financial and accounting platforms which is a perfect job for a data mart.

Holiday campaigns and special events

Your company may be running a short-term special stint or is running a season campaign. And you want to run exclusive analytics on this without contacting your IT or BI team back and forth. A data mart is a quick fix to use and discard here for these special and short-lived events.

Real-time examples for data warehouses

Data warehouses are seen as more than a repository for historical data. Following are some of the ways businesses use them for growth.

To use as a single source of truth

The siloed nature of organizations and the constant in and outflow of data calls for a central truth version. This version will show an unbiased view of the company’s performance from multiple perspectives. This is stored in one central location and gets updated frequently. A data warehouse can perfectly do that, no matter how large the company is or how complex its data architecture is.

To generate complex business reports 

Even though data warehouses store data in large amounts, they can be designed for optimized query performance. This means the business intelligence team can run queries, look for data they want, and create reports faster than ever. Many companies use data warehouses to fulfill their dynamic reporting needs. 

Data warehouse vs data marts. Which one should you choose? 

It ultimately depends on your current data, what you want to measure, the scalability you require, budget, and other considerations. Answering these questions and a few others will decide whether you need a data mart or a data warehouse. 

From a completely different angle, comparing them is like comparing apples to oranges. Both have specific characteristics to offer and use cases to serve.

For starters, a brief consultation call with our adept data engineering team can help you figure out the best solution as one size never fits all in data mart and data warehouse architecture.

The same rule applies to choosing the right approach - Inmon or Kimball. By finding answers on parameters related to your organization’s reporting strategies, leadership and team culture, current budget, and timelines, we can decide and suggest the best-suited custom data warehouse architecture for you.

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