Unlike a data warehouse that stores enterprise-wide data, a data mart includes information related to a particular department or subject area. For instance, sales data mart may contain data related to products, clients, and sales only. Let’s define data marts and get a proper understanding of the question, ‘what is a data mart in a data warehouse?’
Data Mart Definition: What are Data Marts?
Data marts are defined as the smaller version of the data warehouse that deals with one single matter. They are often constructed and managed by a single business department. Since 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 condensed and less intricate than data warehouses, making data marts easier to construct and maintain.
Now that we’ve understood the data mart definition, 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.
Data Mart Benefits for Database Management
Before we discuss the various types of data marts, let’s briefly look at some of the benefits of data marts and why they are necessary for a data-driven business.
- 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 a data mart and more cost-effective than 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.
- A data mart differs from a data warehouse in the way data is partitioned and segmented, which allows granular access control rights.
In summary, data marts are a lot faster, adaptable, and cost-effective to maintain than a data warehouse. Data warehouses are created for consolidating data from a myriad of sources (often not in a structured format).
On the other hand, data marts are used by a single business unit to store its information. Let’s say sales or market departments have to store their business data. They will use a data mart to store it. When the information needs to be visualized by the higher-ups, it will be loaded to a data warehouse and then transformed into insights using BI software.
Types of Data Marts
Data marts can be classified into three main types:
1. Dependent Data Mart
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 warehouses and 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 the 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 data mart 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.
2. Independent Data Mart
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 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).
3. Hybrid Data Mart
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, such as 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 a 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.
Designing Data Marts for Data Warehousing
Database structures and item names into corporate expressions so that non-technical operator can easily use the data mart. If necessary, you can also set up API and interfaces to simplify data access.
The first step is to create a robust design. Some critical processes involved in this phase include collecting the corporate and technical requirements, identifying data sources, choosing a suitable data subset, and designing the logical layout (database schema) and physical structure.
The next step is to construct the data mart. This includes creating the physical database and the logical structures. In this phase, you’ll build the tables, fields, indexes, and access controls.
3. Populate/Data Transfer
The next step is to populate the data mart, which means transferring data into it. In this phase, you can also set the frequency of data transfer, such as daily or weekly. Keeping information in the data mart and warehouse clean is usually over-written every time the data mart is populated.
This step usually involves extracting source information, cleaning and transforming the data, and loading it into the data mart.
4. Data Access
In this step, the data loaded into the data mart is used in querying, generating reports, graphs, and publishing. The main task involved in this phase is setting up a meta-layer and translating 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 than a data warehouse. It also encloses past data so your data analysts can easily determine data trends.