Data Marts: 2025 guide

Access to lots of analytical data sounds great, until you realize teams could spend hours combing through storage to find what they need. Data marts are smaller, use-case-specific content databases that stop your team from getting overwhelmed by irrelevant records. 

In this guide, we discuss what data marts are, how they work, and how they provide teams with more convenience, control, and precision in analytics. 

What is a data mart?

A data mart is a specialized, subject-oriented data storage system that typically supports one or two specific business domains. Rather than being a central repository, data marts hold cleaned, transformed, and analysis-ready information for a particular group of users. By reducing the scope of content stored, they make it easier for teams to find the insight they need.

Since they only offer a subset of information, searching through a data mart is far simpler than larger storage options. What you’re looking for is usually only a few clicks away.

How does a data mart work?

After you ingest and transform information and load it into a data warehouse, you would then aggregate a defined subset before moving it to a data mart. 

Data marts used to always be entirely independent systems, but being separated from other storage methods often led to significant data silos. While it’s still possible to set up independent data marts, nowadays you can also house them within a data warehouse to boost collaboration while retaining accessibility and precision.

Different teams typically identify the information that’s most important to them, helping organize their data mart around an analytical need. Instead of browsing through a massive cloud warehouse, the business unit only has to interact with the data mart when looking for analytics. 

3 types of data mart

All data marts store a subset of an organization’s data, but not all of them work in the same way. Here are the three main types and the differences between them.

1. Dependent data marts

Dependent data marts live inside your cloud warehouse. The mart doesn’t collect any data itself; it instead replicates data from the warehouse and stores it in separate tables. Teams use this approach to reduce data silos and improve visibility.

2. Independent data marts

Independent data marts are completely separate from other storage systems. For this reason, the mart needs to extract data directly from external sources to populate its stores. While this can make the system more agile, it can cause silos without careful monitoring.

3. Hybrid data marts

Hybrid data marts source information from both external systems and a central warehouse. Teams often use hybrid marts to collect input from new sources and test them before integrating with centralized storage. 

How to implement a data mart: 6 steps

Here are six steps to follow when constructing a data mart architecture:

1. Define business requirements and scope: Begin by outlining the team, use case, and key analytics objectives that the data mart will support.
2. Identify data sources and subsets: Once you know the mart’s intended purpose, comb through your data warehouse to determine the main sources and information you’ll use to populate it.
3. Design the logical and physical model: Data marts can have a range of structures, each with unique benefits. Select the right option for you, whether denormalized or star schema, and the logic that will underpin how you interact with content.
4. Move everything over using a data pipeline: Now it’s time to move data from your sources to the mart. Fivetran’s automated extract, load, and transform (ELT) pipelines will speed up and simplify this step significantly.
5. Deploy an analytics layer: Connect dashboards or analytics systems that your users can interact with to query information in your data mart.
6. Monitor, optimize, and evolve: Continually track usage and performance to see where you can improve on your current system. Adapting the system to meet users’ needs will keep productivity high. 

Integrating data marts with data warehouses

While independent data marts are less common, some businesses still deploy them. Opting for dependent marts instead and positioning them within a data warehouse means you don’t have to construct new pipelines for every new mart you build. Every business unit could have a use-case-specific mart for their unique purposes. Coupling warehouses with marts also reduces the need for data duplication, reducing strain on your systems.

The benefits of a data mart

Here are some of the main advantages of using a data mart:

• Faster access: The curated content in data marts is far quicker to search through than the far more comprehensive library of a data warehouse.
• Better decision-making: By aggregating a subset of information, marts provide teams with the most relevant context for decisions. For example, a finance data mart would have all sales and financial reporting information in one place.
• Cost-efficiency: Data marts are purpose-built to fulfill specific business objectives, making them a highly cost-effective storage and analytics strategy.
• Enhanced scalability: Since you can easily add more data or build new ones for other use cases, marts are highly scalable.
• Improved data governance: By centralizing data within marts and warehouses, you create a single source of truth. Improving visibility helps ensure governance is consistently applied across your organization.
• Security and access control: Data marts enable teams to create granular access permissions in line with the principle of least privilege, ensuring that users can only access data required for their jobs.

Challenges and solutions in data mart integration

We’ve collected some of the most common challenges you’re likely to run into when implementing and using data marts and offered some solutions.

Data consistency issues

Your data mart should inherit whatever conventions your warehouse uses. To avoid confusion arising from consistency issues, spend time defining the terminology that your business uses to describe data. Similarly, share transformation logic across all of your marts to keep things as consistent as possible.

Performance bottlenecks

Data marts only work when they’re narrow and specific. If you introduce too many use cases into one space, they can become muddled and experience bottlenecks. Keep your marts tightly scoped to enhance usability.

Complex data integration

Moving data through ingestion pathways manually, especially from multiple sources, is time-consuming, resource-intensive, and tough to scale. By automating much of the process, ELT pipelines dramatically reduce the amount of work required to get data from A to B.

Security and access control

If you don’t also transfer permissions when you move data to a mart, unauthorized parties could end up with access. Be sure to carry over security controls to maintain compliance and full content protection.

How Fivetran supports data mart initiatives

Through its end-to-end ELT pipelines, Fivetran complements data mart efforts by helping you to integrate multiple sources with ease. The platform handles schema management and drift prevention, ensuring your marts stay up-to-date and accurate.

With seamless integration into transformation workflows, you can model the information you deliver to each mart ahead of time. And with reverse ETL (rETL), curated data marts can even send content back to your centralized repository to keep the entire organization on the same page.

Learn how to use Fivetran to power your data architecture by requesting a demo today. 

FAQs

What are some examples of data marts?

A cloud data mart can support a wide range of business units, including marketing, sales, and HR. You can even use a data mart diagram to map different teams’ storage facilities within their business unit.

How do you create an SQL data mart?

Data mart software allows you to link marts to analytics platforms. From here, you can use SQL to query the data and structure it using that language. As you’re interacting and building with SQL, it’s a good idea to version your mart so you can roll it back if any errors occur.

Data mart vs. data warehouse comparison

An enterprise data warehouse provides space to store large volumes of information. A data mart is a smaller subsection of a warehouse (though it can also be independent of the warehouse) that stores more specific data sets that support particular use cases.

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