Unlike a data warehouse that stores enterprise-wide data, a data mart includes information related to a particular department or subject area. For instance, a sales data mart may contain data related to products, clients, and sales only.
Data marts are often constructed and managed by a single business department. As they are subject-oriented, data marts typically take data from only a small number of sources, which could be internal operational systems, a centralized data repository, or external sources. Data marts are usually smaller in size and less intricate than data warehouses, which makes them easier to construct and maintain.
In this blog, we’ll cover three different types of data marts and their uses. We’ll also illustrate a step-by-step guide on how to implement a data mart for your business.
Why Do You Need A Data Mart?
Before we discuss the various types of data marts, let’s briefly look at some of the benefits of data marts:
- A data mart enables faster data access by retrieving a specific set of data for BI and reporting. As a result, it helps accelerate business processes.
- Being subject-focused, it’s easier to implement and more cost-effective as compared to building an enterprise data warehouse.
- Using a data mart is easy because it is designed according to the requirements of a particular group of users working in a specific department.
- A data mart is comparatively more adaptable than a data warehouse. Any change in the data model can be easily and quickly incorporated in the data mart because of its smaller size.
- In a data mart, data is partitioned and segmented, which allows granular access control rights.
Types of Data Marts
Data marts can be classified into three main types:
A dependent data mart lets you combine all your business data into a single data warehouse, giving you the typical benefits of centralization.
In case one or multiple physical data marts are needed, you’ll have to build them as dependent data marts to ensure consistency and integration across all data storage systems.
Dependent data marts can be constructed using two different approaches. In the first approach, enterprise data warehouse, as well as data marts, are built so the operator can access both, whenever needed. In the second approach, also known as the federated approach, the results of ETL process are stored in a temporary storage area such as a common data bus instead of a physical database so the operator can only access the data mart.
The latter methodology is not ideal as it occasionally yields a data junkyard in which all data originates from a shared source, but it’s mostly discarded.
An independent data mart can be created without using the central data warehouse. It is mostly recommended for smaller units or groups within an organization. As the name suggests, this kind of data mart is neither related to the enterprise data warehouse nor to any other data mart. It inputs data separately and the analyses are also executed independently.
As more and more independent data marts are constructed, the data redundancy also increases across the organization. This is because every independent data mart needs its own, usually a duplicate copy of the comprehensive business information. As these data marts directly access files and/or tables of the operational system, they considerably limit the scalability of the decision support systems (DSS).
By using a hybrid data mart, you can combine data from several operational source systems in addition to a data warehouse. These data marts are particularly useful when you require ad hoc integration, for instance, after adding a new group or products to the business.
As the name indicates, a hybrid data mart is a mixture of dependent and independent data marts. It’s suitable for businesses that have multiple databases and need quick turnaround. A hybrid data mart needs slight data cleaning, supports huge storage structures, and is flexible as it combines the benefits of both dependent and independent data marts.
database structures and item names into corporate expressions so that non-technical operators can easily use the data mart. If necessary, you can also set up API and interfaces to simplify data access.
The last step involves managing the data mart which includes:
- Controlling ongoing user access
- Optimization and refinement of the target system for improved performance
- Addition and management of new data into the data mart
- Configuring recovery settings and ensuring system availability in the event of failure
The Bottom Line
A data mart includes a subsection of enterprise-wide data, which is valuable to a particular user group in the organization. Unlike a data warehouse that’s expensive and complex to create, a data mart offers a cost-efficient alternative. It allows faster data access and is simple to use; as it’s precisely designed according to the operators’ requirements and focuses on a single department/subject area.
A data mart can help fast-track your corporate processes; as it takes less time to implement as compared to a data warehouse. It also encloses past data so your data analysts can easily determine data trends.