Upcoming Webinar

Join us for a FREE Webinar on Automated Processing of Healthcare EDI Files with Astera

June 27, 2024 — 11 am PT / 1 pm CT / 2 pm ET


Home / Blogs / What is a Data Mart? Design, Examples, and Implementation Explained

Table of Content
The Automated, No-Code Data Stack

Learn how Astera Data Stack can simplify and streamline your enterprise’s data management.

What is a Data Mart? Design, Examples, and Implementation Explained

December 19th, 2023

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. Read this blog to develop a better understanding of these departmental data repositories.

What are Data Marts?

Data marts are a subset 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, they typically take data from only a small number of sources, which could be internal operational systems, data lake, a centralized data repository, or external sources. They are usually condensed and less intricate than data warehouses, which makes them easier to construct and maintain.

Now that we’ve understood what they are, we will look at the three different types of data mart examples, their uses, and how they simplify data management. We’ll also illustrate a step-by-step guide on how to implement a department-specific repository of data for your specific business.

Types of data marts

Source: Study.com

How Do Data Marts Benefit Database Management?

Before we discuss their various types, let’s briefly look at the benefits of data marts and why they are necessary for a data-driven business:

  • Enable faster data access by retrieving a specific set of data for BI and reporting. As a result, it helps accelerate business intelligence.
  • They are easier to implement and more cost-effective than building an enterprise data warehouse.
  • Designed according to the requirements of a particular group of users working in a specific department.
  • They are comparatively more adaptable than a data warehouse. Any change in the data model can be easily and quickly incorporated into the data mart because of its smaller size.
  • Allow granular access control rights because of extensive partitioning and segmentation.

In short, they are a lot faster, adaptable, and cost-effective to maintain than a data warehouse. By contrast, data warehouses are created for consolidating data from a myriad of sources (often not in a structured format).

Types of Data Marts

Data marts can be classified into three main types:

1. Dependent

A dependent data mart lets you combine all your business data into a single data warehouse, giving you the typical benefits of centralization.

In this example, departmental data stores are needed and you will have to build them as dependent entities 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 departmental data.

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.

2. Independent

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 repository is neither related to the enterprise data warehouse nor any other entity. 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 store needs its own, usually a duplicate copy of the comprehensive business information. As these independent stores of data directly access files and/or tables of the operational system, they considerably limit the scalability of the decision support systems (DSS).

3. Hybrid

By using a hybrid data mart, you can combine data from several operational source systems in addition to a data warehouse. These 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 both the dependent and independent types. It is suitable for businesses that have multiple databases and need a quick turnaround. Data mart examples of this type need slight data cleaning, support huge storage structures, and are flexible as it combines the benefits of both dependent and independent systems.

Designing Data Marts for Data Warehousing

Here’s how you can go about designing a data mart for your enterprise needs:

1. Design

The first step is to create a robust design. Some critical processes involved in this phase are:

  • Collecting the corporate and technical requirements.
  • Identifying data sources.
  • Choosing a suitable data subset.
  • Designing the logical layout (database schema) and physical structure.

2. Build/Construct

The next step in the process is to construct the data mart. This includes creating the physical database and the logical structures. In this phase, you’ll build the fact tables, dimension tables, fields, indexes, and access controls.

3. Populate/Data Transfer

The next step is to populate the mart, which means transferring data into it. In this phase, you can also set the frequency of data transfer, such as daily or weekly. This step usually involves:

  • Extracting source information.
  • Cleaning and transforming the data.
  • Loading the data into the departmental repository.
  • Building the metadata and storing it.

4. Data Access

In this step, the data loaded into the data mart is used in querying, generating reports, graphs, and publishing. The main tasks involved in this phase are:

  • 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.
  • Setting up and maintaining database structures.
  • If necessary, you can also set up API and interfaces to simplify data access.

5. Manage

The last step involves management and observation, which includes:

  • Controlling ongoing user access.
  • Optimization and refinement of the target system for improved performance.
  • Addition and management of new data into the repository.
  • Configuring recovery settings and ensuring system availability in the event of failure.

Looking at The Future – Data Marts and the Cloud

Data marts offer an efficient, flexible, and scalable approach to storing data. However, on-premise solutions are quickly becoming obsolete due to the growing amounts of incoming data. As a result, many businesses are moving their data storage solutions to the cloud.

A cloud-based architecture enables businesses to create and store their data online. The speed and flexibility of this approach create an opportunity for analytics in the cloud. Performing analytics in the cloud allows for greater scalability and cost-efficiency compared to on-premise options. That’s not all. Cloud-based data marts offer other numerous benefits as well:

  • Real-time data analytics.
  • On-demand access to data.
  • A unified view of all data marts.
  • Access to cloud-native sources and destinations.

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, it offers a cost-efficient alternative. It also 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.

Data Science vs. Data Analytics: Key Differences
What is OLAP (Online Analytical Processing)?
What Is Database Schema? A Comprehensive Guide
Considering Astera For Your Data Management Needs?

Establish code-free connectivity with your enterprise applications, databases, and cloud applications to integrate all your data.

Let’s Connect Now!