For accurate business intelligence, companies rely on data warehouses and data marts. They serve as a centralized repository, storing existing and historical data for analysis and data-driven business decisions. So, what’s the difference between these two data repositories?
In this blog, you’ll find the answer to the questions, what a data mart is in data warehousing and the pros and cons of choosing a data warehouse and data mart.
Data Warehouse – An Overview
A data warehouse is a structure that consolidates data from multiple source systems. The primary purpose of a centralized data warehouse is to offer a correlation between data from different data source systems, for instance, product information stored in one system and purchase order data stored in another system.
Another common purpose of a data warehouse is to support business intelligence (BI) and perform queries and analyses. Data warehouses can be used in different organizational settings. An example of a data warehouse for the finance department could be endowments, account balances, accounting transaction details, etc.
Some people often get confused between a data warehouse and a database. It is essential to distinguish between a data warehouse and a database. A data warehouse acts as a top layer on a database and takes the information in different databases to create a layer for analytics.
A data warehouse is used for online analytical processing (OLAP), which involves complex querying to analyze transactions. It’s an essential element of business intelligence. It stores a large amount of data at a single location, which is then used to extract essential insights and streamline business processes. Thus, it helps support the decision-making process of companies.
While selecting a data warehouse solution, it is important to compare the features of various tools offered in the market.
Data Mart – An Overview
A data mart is a subset of a data warehouse typically used to access customer-facing information. It is a structure that is specific to data warehousing settings. Thus, a data mart is usually focused on a business line or team and draws information from only a particular source.
Unlike implementing an enterprise data warehouse that may extend to several months or even years, a data mart is usually implemented within a few months, providing quick support. This is due to the data mart’s smaller size (less than 100GB) and data extraction from a lesser number of sources.
An enterprise data mart is preferred for departmental analysis and reporting activities, such as sales, marketing, finance, etc. These activities are usually performed in a dedicated business unit. Therefore, enterprise-wide data is not required for BI.
An example of a data mart could be a marketing specialist using a dedicated data mart to perform market analysis and reporting. However, designing a data mart architecture is a lengthy and costly process, but errors can be reduced by following the widely used best practices for scalable data mart architecture design.
Based on their requirements, companies can use several data marts for different departments and opt for data mart consolidation by merging various marts to construct a single data warehouse later. This approach is called Kimball’s dimensional design method. Another method, known as Inmon’s approach, is to design a data warehouse first and then create several data marts for particular departments, as required.
Because of time and budget limitations, businesses usually opt for the Kimball approach.
Types of Data Marts
The two main types of data mart are:
1- Independent Data Mart
An independent data mart architecture is built without a data warehouse. They serve as a stand-alone system and are easy to develop for short-term goals. However, each independent data mart comes with its separate ETL tool and logic; therefore, they become hard to manage as businesses expand.
2- Dependent Data Mart
A dependent data mart is built using an existing enterprise data warehouse. It takes a top-down approach that starts with saving all business data in a single central location and then extracts a specific part of the data when required for analysis.
Data Mart vs. Data Warehouse: Explained with Examples
The major differences between a data marts vs data warehouse are summarized in the table below:
| Data Warehouse | Data Mart |
| A data warehouse is used to store data from numerous subject areas. | A data mart carries data related to a department, such as HR, marketing, finance data mart, etc. |
| It acts as a central data repository for a company. | It’s a logical subsection of a data warehouse in which the data is deposited in inexpensive servers for particular departmental applications. |
| A data warehouse is designed using star, snowflake, galaxy, or fact constellation schema. However, a star schema is the one that is used most widely. | A database mart uses a star schema for designing tables. |
| It’s tricky to design and use a data warehouse because it usually includes a large amount of data, more than 100GB. | Designing and using a data mart is comparatively more manageable because of its small size (less than 100GB). |
| A data warehouse is designed to support the decision-making process in a company. Thus, it offers an enterprise-wide understanding of a centralized system and its autonomy. | A data mart is designed for particular user groups or corporate departments. Thus, it offers departmental interpretation and decentralized data storage. |
| A data warehouse is used to store detailed information in denormalized or normalized form. | A data mart holds highly denormalized data in a summarized form. |
| A data warehouse has large dimensions and integrates data from many sources, which may cause a risk of failure. | A data mart has smaller dimensions to integrate data sets from a smaller number of sources, so there’s less risk of failure. |
| A data warehouse is subject-oriented and time-variant in which data exists for a longer duration. | A data mart is used for particular areas related to a business, and it retains data for a shorter duration. |
Most people fail to differentiate between data warehouse and data mart. However, we hope you will now tell the difference between the two using the side-by-side comparison above.
Data Mart vs. Data Warehouse vs. Data Mining
It is also essential to note the differences among data mining, database marts, and data warehouses. Data mining is defined as the process of extracting data from an organization’s multiple databases and re-purposing or re-organizing that data for other tasks. On the other hand, a data warehouse acts as a storage system to keep or store data for easy mining. Lastly, a data mart is a subset of a data warehouse catering to a specific business or departmental usage.
The Difference in Business Use Cases
A data warehouse contains data from various business functions, making it significant for cross-departmental analyses. For example, businesses could build a customer 360 profile that unifies multichannel data, such as CRM, social media, retail, etc. Business analytics experts can provide strategic, in-depth insights into customers’ needs and preferences by consolidating this data.
On the other hand, a data mart comprises limited information about a business department or function. For instance, if a manufacturing manager wants to get to the bottom of production delays, the manager can visit the data mart, query the data, and run reports to know where the error lies in the production line. The limited scope of data helps the manager swiftly extract and analyze the data without any unnecessary delays.
Summing Up the Difference
The operator is offered one integrated platform in a data warehouse where decision support queries can be performed easily. On the other hand, a data mart offers a departmental interpretation of the stored data.
For example, a specialist from your finance department can use a financial data mart to perform fiscal reporting. However, suppose your business is looking forward to expanding. In that case, it requires a data warehouse because it’ll have to integrate data from several sources across the enterprise to make an informed decision. The ideal data repository for an organization is the one that fits the business requirements.
Astera Data Warehouse Builder is an enterprise data warehouse tool. It offers an all-in-one platform to design, build, and test on-premise and cloud data warehouses from scratch and automate the entire processes to derive insights faster, without writing a single line of code.