Data marts vs. Data products: What's the difference?

As data organizations mature, they often start using similar terms to describe very different ideas. Two of the most commonly conflated concepts are data marts and data products. Both are critical to delivering reliable, scalable analytics, but they serve different purposes and solve different problems. Understanding how they differ helps teams design better data architectures, clarify ownership, and align technical work with business outcomes.

In this article, we’ll break down what data marts are, what data products represent, and how the two work together to support trusted, self‑service data at scale.

Understanding data marts

Data marts represent a specific layer in the data transformation pipeline. In dbt projects, marts form the final layer of core transformations, where staging models and intermediate models come together to create business-defined entities. Each mart represents a specific entity or concept at its unique grain: an order, a customer, a payment, or a click event.

The defining characteristic of marts is their entity-grained structure. A customers mart contains all useful data about customers at the customer level. An orders mart maintains individual orders as its core grain, even while incorporating data from other entities like users or products. This granular approach provides flexibility for downstream analysis without pre-aggregating data or answering specific business questions within the transformation layer itself.

In modern data warehousing, where storage costs less than compute, marts embrace denormalization. Rather than maintaining strict normalization like traditional Kimball star schemas, marts borrow and add relevant data from other concepts. Building the same data in multiple places (such as including order information within a customers mart) proves more efficient than repeatedly rejoining these concepts during analysis. This represents a fundamental shift in how data teams balance storage against computational efficiency.

Structural characteristics of marts

Marts follow consistent organizational patterns within dbt projects. They typically group by department or business area (finance, marketing, operations) rather than by source system. File naming uses plain English based on the entity: customers.sql, orders.sql, payments.sql. This clarity helps anyone navigating the project understand what each mart contains.

The models themselves are materialized as tables or incremental models rather than views. This approach gives end users faster query performance and reduces costs by avoiding repeated computation of entire model chains. Teams generally start with views, move to tables when query performance degrades, and finally implement incremental materialization when full table rebuilds become too slow.

Marts avoid building the same concept differently for different teams. Creating finance_orders and marketing_orders typically signals an anti-pattern, though legitimate exceptions exist (such as when finance requires government reporting that diverges from standard revenue measurement). The key is ensuring these represent genuinely separate concepts rather than departmental perspectives on identical data.

Understanding data products

Data products represent a fundamentally different concept. Rather than describing a technical layer in the transformation pipeline, data products describe how teams manage and reason about their most important business processes in data. A data product groups related data assets (often spanning multiple marts, staging models, and source tables) into a cohesive unit with clear ownership, quality standards, and business purpose.

The data product lens provides a management framework for business-critical data. Teams organize data products into functional areas like business intelligence, finance, or operations. Each product receives a priority level indicating its importance, which determines how urgently teams address issues. This organizational structure brings transparency: stakeholders can instantly see whether errors exist on or upstream of critical data products.

Ownership and accountability

Data products establish clear ownership patterns that extend beyond the data team. While analytics engineers might own core transformation models, ownership distributes across the organization based on business function. Finance teams receive notifications about issues with finance data products. Operations managers get alerted when problems affect operational datasets they're responsible for maintaining.

This distributed ownership model changes how organizations think about data quality. Rather than treating data quality as solely the data team's responsibility, data products create shared accountability. Business stakeholders become active participants in maintaining the reliability of data assets they depend on.

Reliability and monitoring

Data products incorporate comprehensive monitoring and testing strategies. Teams combine dbt tests with automated anomaly detection to catch both known issues (through explicit tests) and unexpected problems (through pattern-based monitoring). This dual approach significantly reduces detection time; organizations report resolving most issues within minutes rather than hours.

The reliability workflow for data products spans detection, resolution, and learning. When issues occur, teams declare incidents that become part of a knowledge base. This historical record helps teams respond faster when similar problems recur, turning incident management into organizational learning.

Key differences in practice

The distinction between marts and data products manifests in several practical ways. Marts represent technical artifacts: SQL files that transform data according to specific patterns and conventions. Data products represent business concepts: groupings of related data assets managed as coherent units with defined quality standards and ownership.

Marts exist within a single layer of the transformation pipeline. A customers mart or orders mart lives in the models/marts directory of a dbt project. Data products span multiple layers, encompassing source tables, staging models, intermediate transformations, and multiple marts that together support a business function.

Marts follow technical naming conventions like dim_customers or fct_orders that indicate their role in dimensional modeling. Data products use business-oriented names that reflect their purpose: "Customer Analytics," "Financial Reporting," or "Operational KPIs."

The scope differs fundamentally. A single data product might include dozens of marts alongside their upstream dependencies. Conversely, a single mart might contribute to multiple data products serving different business functions.

Architectural implications

These differences shape how teams architect their data platforms. Marts require careful attention to grain, denormalization strategy, and materialization approach. Teams must decide when to build separate marts versus when to reference existing ones, balancing performance against maintainability.

Data products require governance frameworks that marts alone don't address. Teams need processes for defining product boundaries, assigning ownership, setting quality standards, and managing incidents. This governance layer sits above the technical transformation work, providing business context and accountability structures.

The relationship between marts and data products isn't hierarchical; it's complementary. Well-designed marts provide the building blocks for reliable data products. Clear data product definitions guide decisions about which marts to build and how to structure them. Teams need both concepts to deliver trusted data at scale.

The role of the Semantic Layer

The distinction between marts and data products becomes more nuanced when considering dbt's Semantic Layer. Without the Semantic Layer, teams typically build heavily denormalized marts optimized for direct consumption by business users. With the Semantic Layer, teams maintain more normalized marts, allowing MetricFlow flexibility in how it combines and aggregates data.

This architectural choice affects how data products are constructed. In projects without the Semantic Layer, data products might consist primarily of wide, denormalized marts ready for immediate analysis. With the Semantic Layer, data products include normalized marts plus the semantic definitions that specify how to calculate metrics and combine entities.

Practical guidance for data leaders

Data engineering leaders should think about marts and data products as addressing different concerns. Invest in marts when focusing on transformation architecture: how to structure models, what grain to maintain, how to optimize performance. Invest in data products when focusing on business value: what data assets matter most, who owns them, how to ensure reliability.

Both concepts deserve attention, but at different times and for different purposes. Early in a data platform's lifecycle, establishing solid mart patterns matters most. As the platform matures and complexity grows, layering on data product thinking helps manage that complexity and maintain business alignment.

The most successful data organizations use marts as their technical foundation and data products as their management framework. Marts provide the modular, well-tested building blocks. Data products provide the business context, ownership, and quality standards that turn those building blocks into trusted assets stakeholders rely on.

Understanding this distinction helps data leaders communicate more effectively with both technical teams and business stakeholders. When discussing transformation architecture with analytics engineers, talk about marts. When discussing data strategy with business leaders, talk about data products. Both conversations matter, but they serve different purposes in building a mature data platform.

Related resources:

• Marts: Business-defined entities - Learn best practices for structuring marts in dbt projects
• Understanding data modeling - Explore the fundamentals of data modeling and transformation
• Modular data modeling techniques - Discover how to build maintainable, modular data models
• Building reliable data products - See how organizations implement data products in practice

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