Data warehouse vs. data mart: Key differences and use cases

Scope and purpose of data warehouses and data marts

Understanding the difference between a data warehouse and a data mart starts with knowing the role of the data warehouse. It is a company-wide repository of business data, sometimes referred to as a single source of truth. It integrates data from across the organization, such as sales, finance and HR, using a consistent schema. The schemas are typically based on dimensional modeling and built from operational sources such as CRM, ERP and logs.

A data mart is a focused subset of data that serves specific business units or departmental use cases such as sales, production or marketing.

For example, a marketing team might be frustrated by delays in IT's implementation of changes to the main warehouse. They want their customer segmentation data now, not next quarter. Meanwhile, the CFO's office needs that same customer data integrated with revenue recognition processes.

A data mart supports the marketing department's need to analyze campaign performance, lead conversion and channel attribution. If that data is buried in a complex schema joined with unrelated data sets, it slows the marketing team's work with that data down. By contrast, a data warehouse is better suited to meet the CFO's requirements for completeness and governance in this example.

There are several key differences between data warehouses and data marts to consider, including data volume and scope, ownership models and integration and architecture.

Data volume and scope

An enterprise warehouse might store many terabytes, or even petabytes, of historical data spanning from all business functions over years. In contrast, a typical data mart is much smaller, often just a terabyte in size. In the preceding example, the data mart could contain only two years of marketing data with pre-aggregated metrics.

When marketing teams ask for "just their data," they're seeking a manageable, focused data set that they can quickly iterate on. A data mart is purpose-built for consumption. It's typically optimized for specific queries and pre-filtered and pre-aggregated with metrics that the team cares about.

Ownership models

A data warehouse requires centralized governance where the IT department controls the schema, extract, transform and load (ETL) processes and data quality standards. A data mart, in contrast, can have more distributed ownership. For example, marketing might define the calculations, metrics and rules used in their departmental mart, while IT maintains the technical infrastructure.

Flexible governance is essential in these distributed ownership scenarios. The model of distributed ownership is both an opportunity and a risk. Some organizations struggle with data marts becoming silos because they are overly departmentally focused.

Integration and architecture

Successful architectures treat data marts as extensions of the data warehouse rather than replacements. They maintain clear standards for integration with the data warehouse, but they enable a degree of freedom in how departments use focused data sets.

A dependent data mart acts as a performance layer built on top of the warehouse. In some cases, it is simply a set of database views tailored to specific use cases.

A data architect could, by contrast, build an independent data mart with its own ETL processes for data extraction and loading from source systems. This can cause problems, especially when the mart overlaps with a central warehouse or other marts. These include duplicate ETL logic, inconsistent business rules and conflicting versions of the truth.

Independent marts have valid use cases, especially for specialized analytics that need real-time data or external data sources that don't fit the enterprise warehouse schema.

These differences span multiple dimensions:

Access patterns and use cases

A successful strategy depends on understanding how different teams use data. An executive dashboard might pull data from the warehouse monthly for board reporting. In that case, high latency might be acceptable, while accuracy and completeness matter most. However, an e-commerce team might need to adjust pricing hourly based on inventory and competitor data. These fundamentally different access patterns influence architecture decisions.

A data warehouse excels at combining data from multiple departments to support historical trend analysis and regulatory reporting. In these uses, IT must track where data comes from, also called data lineage. A data mart suits operational analytics, self-service BI and rapid prototyping of new analytical approaches.

For example, a retailer needs to analyze the effect of loyalty program changes. The warehouse gives a comprehensive view of customer lifetime value, cross-functional effect on inventory and supply chain, and integration with financial reporting. In contrast, marketing creates a data mart focused just on campaign performance and customer segmentation to test and adjust their messaging weekly.

Cost implications

Combining data warehouses and data marts might sound like the best of both worlds, but it raises cost concerns. Does it mean essentially paying twice for data infrastructure?

Data warehouse cloud platforms enable twice the data platforms without twice the hardware, licensing and maintenance of resources. In a cloud-native data warehouse environment, a data mart can be just a different database schema with compute resources that scale down when not in use.

Faster decision-making often yields a return that justifies the incremental cost of both warehouses and marts. For example, a marketing team's ability to optimize campaigns weekly instead of monthly can improve customer acquisition costs enough to offset any additional cloud costs for the data architecture.

Data lakehouses and data modernization

In addition to data warehouses and data marts, many businesses deploy concepts such as data lakes and lakehouses. These approaches make some of the distinctions in architecture less rigid.

A data lakehouse combines the capabilities of a data lake and a data warehouse in a single platform. It can store raw, unstructured data in a data lake, maintain a structured warehouse for enterprise reporting and create mart-like views that combine structured and unstructured data without physically moving data. This flexibility has business appeal.

Traditionally, if a customer service team wanted to analyze support tickets alongside transaction data, for instance, the architect had to decide between pulling unstructured text into the warehouse or building a separate mart. With a lakehouse approach, architects can create analytical views that span both systems.

The differences in development time can be significant. A conventional data mart might take several months to design, build and test. With a cloud-based lakehouse approach, teams are able to prototype a mart-like analytical view in days and put it into production in weeks.

Standalone data marts are becoming less common in modern, cloud-based lakehouse architecture. In fact, many data architects believe that a lakehouse itself can often serve both roles. Still, for business analysts, having a departmental, focused data set -- whether physically separated or a logical view -- is vital for performance and experimentation.

Strategic recommendations for data architecture

Many organizations can benefit from both data warehouses and data marts, provided each is designed and maintained with clear purposes.

The data warehouse should be a system of record for enterprise reporting, especially regulatory or financial reporting and for cross-functional analytics.

Data marts often function as a testing ground where teams can experiment and refine their analytics before applying successful patterns to the warehouse. To make this feasible, organizations should architect marts as extensions of, not alternatives to, the warehouse. This means dependent marts that maintain data lineage and governance standards while providing the speed and flexibility business teams need.

Practically, choosing between data warehouses and data marts is not an either-or decision. These architectures are complementary in a data ecosystem where each component serves its purpose.

Donald Farmer is a data strategist with 30-plus years of experience, including as a product team leader at Microsoft and Qlik. He advises global clients on data, analytics, AI and innovation strategy, with expertise spanning from tech giants to startups. He lives in an experimental woodland home near Seattle.