Suresh
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.
Factor | Data warehouses | Data marts |
Storage size | Meant to store data in larger amounts, storage starts from 100 GB and reaches up to 1TB. | Comparatively smaller in size than data warehouses. Storage size less than 100GB |
Complexities in setting up | It takes proper planning and higher effort to build a data warehouse. | Much easier to set up |
Where do they get data from? | Collects data from multiple data sources like IoT applications, ERP, etc. | Collects data from another data warehouse |
Top-down or bottom-up approach? | Top-down as they are centralized. | Bottom-up as they are decentralized. |
Purpose | Powers the reporting needs of a company | Powers the reporting needs of a specific functional area, process, or short-term campaign. |
Implementation costs | Costs will be on the higher side, due to the complicated and time-consuming setup process. | Costs much less to set up a data mart and get it going. |
Implementation time | Can take from months to years. | Can take weeks to months. |
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.
Inmon approach | Kimball approach |
Follows top-down approach | Follows bottom-up approach |
Due to its flexibility, best for data sources with a higher probability of change | Best for data sources that are stable |
Is data-driven | Is user or process-driven |
Easier to maintain | Difficult to maintain |
Creates a logical model data warehouse | Creates a dimensional model data warehouse |
Data redundancy and inconsistency are avoided since it applies normalized form while updating the data warehouse. | Redundant data might be present due to the denormalization approach being applied. |
Data warehouse acts like a single source of truth | Data warehouse is not exactly a single source of truth in this approach. |
Can take time to build | Quick to setup |
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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