What is a data mart? How to build a cloud analytics layer

A data mart is a focused, subject oriented subset of enterprise data designed for a specific department or business function. In modern cloud environments, it is not simply a smaller database. Done well, it becomes a governed analytics layer that gives teams faster access to trusted data without creating another silo.

For enterprises using Snowflake, Amazon Redshift, Google BigQuery or Azure Synapse, data mart architecture should be considered as part of the wider cloud data platform and governance model.

What is a data mart?

A data mart is a curated data store built around the analytical needs of a specific business area, such as finance, sales, marketing or operations.

Instead of giving analysts access to thousands of tables in an enterprise data warehouse, a data mart presents a smaller, business ready set of facts, dimensions and metrics.

A finance data mart might contain revenue, costs and budgets. A sales data mart could combine CRM opportunities, orders and targets. A marketing data mart may focus on campaigns, acquisition and conversion.

The important word is curated.

A data mart should provide:

• clear definitions
• consistent metrics
• appropriate access permissions
• tested transformations
• understandable data models
• reliable refresh processes

This makes data marts useful for self service analytics. Business users can work with trusted data without needing to understand the complete enterprise architecture behind it.

We see a data mart as an architectural layer between enterprise data sources and analytical consumption.

That is also why it fits naturally into a wider Business Intelligence environment. In our guide to Business Intelligence vs Data Analytics, we explain how governed data and BI tools work together to support repeatable decision making.

Data mart vs data warehouse vs data lake

A data mart serves a specific business domain, a data warehouse integrates enterprise data, and a data lake stores broader volumes of raw or semi structured information.

A data warehouse provides a common analytical foundation across the enterprise.

A data mart narrows that foundation to a specific business context.

A data lake is designed for broader and often less structured data, including logs, events or documents.

A modern architecture may use all three:

Data lake or lakehouse → transformation layer → governed data marts → BI and analytics

The data lake provides flexibility. The transformation layer standardizes information. Data marts make specific domains easier to consume.

The three types of data marts

Data marts are usually classified as dependent, independent or hybrid.

Dependent data mart

A dependent data mart receives data from a central enterprise data warehouse.

This model works well when consistency and governance matter. Departments can reuse common definitions for customers, products, revenue or organizational structures.

For larger enterprises, we generally prefer this approach when a mature data platform already exists.

Independent data mart

An independent data mart is built directly from operational or external systems.

It can accelerate analytics when no central platform is available, but it also creates a greater risk of duplicated logic and inconsistent KPIs.

Hybrid data mart

A hybrid data mart combines governed enterprise data with domain specific sources.

For example, a marketing mart may reuse standardized customer data while adding advertising platform data.

For many cloud environments, this provides a practical balance between central governance and domain autonomy.

How data mart architecture works?

Most analytical data marts use dimensional models that make business queries easier to understand and maintain.

The most common approach is the star schema.

A central fact table stores measurable events such as transactions or orders. It connects to dimension tables describing customers, products, dates or regions.

For example:

Sales fact → customer dimension → product dimension → date dimension

A snowflake schema normalizes dimensions into additional related tables. This can reduce duplication but increases complexity.

More advanced enterprise environments may also use Data Vault patterns for historical traceability and rapidly changing sources.

A common modern architecture looks like this:

Raw data → staging → integration → business transformations → data marts → semantic layer → dashboards

The objective is not to choose the most sophisticated model. It is to create the simplest architecture that remains reliable as data volume and business needs grow.

Why enterprises build data marts?

The main benefit of a data mart is reducing the distance between trusted enterprise data and a business decision.

A well designed data mart offers several advantages.

Faster analytics

Analysts query a focused group of relevant tables instead of navigating the full enterprise warehouse.

Easier self service

Business users work with familiar concepts such as revenue, customers or campaigns rather than technical source system structures.

Consistent metrics

A governed mart can provide one definition of the KPIs used within a business domain.

Stronger access control

Data marts create logical boundaries around information.

A sales team may need customer revenue but not payroll data. Finance may need transaction details that should not be available across the organization.

Faster delivery

A focused data domain can often be implemented sooner than a large enterprise wide warehouse initiative.

That allows organizations to deliver value incrementally without abandoning broader architectural standards.

Building data marts on cloud platforms

On modern cloud data platforms, a data mart is usually a logical and governed layer rather than a completely separate physical database.

Snowflake

Snowflake enables teams to separate data organization from compute.

Departments can work with dedicated schemas and compute resources while sharing governed enterprise data. This reduces the need for unnecessary copies.

Amazon Redshift

Redshift supports marts through schemas, views and materialized views.

Redshift Spectrum can also query data stored in Amazon S3, which supports hybrid patterns combining warehouse and lake data.

Google BigQuery

BigQuery allows organizations to separate domains through projects, datasets and views.

Authorized views can expose selected data without granting access to underlying tables.

Azure Synapse

Azure Synapse combines SQL analytics with the wider Azure ecosystem.

Dedicated and serverless SQL options make it possible to adapt the architecture to different analytical workloads.

Across all four platforms, our recommendation is consistent:

Avoid building a separate data platform for every department.

Instead, create data marts as governed domains within a broader cloud architecture.

If they are part of a wider modernization initiative, our cloud migration strategy framework explains how we connect target architecture, sequencing and governance.

From ETL to ELT

Modern cloud data marts increasingly use ELT, where data is loaded into the cloud platform before business transformations are applied.

Traditional ETL follows this sequence:

Extract → Transform → Load

Modern ELT often looks like:

Sources → ingestion → cloud warehouse → transformations → data marts

Tools such as dbt can organize transformations into clear layers:

• staging models
• intermediate business logic
• mart models
• facts and dimensions

The main benefit is not the tool itself. It is keeping transformation logic version controlled, testable and documented.

That reduces the risk of critical KPI logic living only inside dashboards or spreadsheets.

How to build or migrate a data mart

A successful data mart project should move from business requirements to architecture, pipelines and governance in a controlled sequence.

1. Define the business domain

Start with users, decisions and KPIs rather than database tables.

2. Identify authoritative sources

Map ERP, CRM, operational databases, SaaS platforms and existing warehouse models.

3. Design the target model

Define facts, dimensions, shared metrics and relationships.

4. Design governance

Classify sensitive information and define roles, masking, retention and lineage.

5. Build the ETL or ELT pipeline

Create repeatable ingestion and transformation processes with automated data quality checks where practical.

6. Add the analytics layer

Connect BI tools and semantic models once the underlying definitions are stable.

7. Monitor and improve

Track query performance, freshness, pipeline failures, adoption and data quality.

A mart is not complete because its first dashboard works. It needs ongoing ownership.

For wider cloud modernization, our article Move the business, not the mess explains why we recommend redesigning the target architecture instead of recreating legacy environments in the cloud.

The risks of poorly designed data marts

A governed data mart improves access, but an uncontrolled one creates another data silo.

The biggest problem is duplicated business logic.

Marketing may calculate active customers one way, finance another way and sales a third way. Each report can be technically correct while executives receive different answers.

Other common risks include:

Data redundancy

Multiple marts copy the same datasets and increase maintenance effort.

Data drift

Source systems change while transformations remain unchanged.

Weak lineage

Teams cannot explain where a metric came from.

Performance issues

Poor data models or uncontrolled queries can increase cloud compute cost.

Shadow governance

Sensitive information is exposed because local teams design permissions independently.

The solution is to standardize the elements that should be shared:

• business definitions
• naming conventions
• shared dimensions
• transformation standards
• quality tests
• security policies
• ownership

This creates governed self service analytics rather than uncontrolled data proliferation.

If you are building a new data platform, modernizing an existing warehouse or improving access to trusted information, explore our Business Intelligence and Data Analytics services.

Talk to us about designing a data mart architecture that fits your cloud platform, business domains and governance requirements.

FAQ about data marts

What is a data mart in simple terms?

A data mart is a focused collection of business ready data designed for one department or analytical domain.

What is the difference between a data mart and a data warehouse?

A data warehouse integrates information across the enterprise. A data mart focuses on one business area and presents a simpler subset of that information.

What is an example of a data mart?

A sales data mart could combine CRM opportunities, customer data and orders to measure pipeline, win rates and revenue.

What is a dependent data mart?

A dependent data mart receives its data from a central data warehouse or governed enterprise platform.

What is an independent data mart?

An independent data mart uses operational or external sources without relying on a central warehouse.

Which cloud platforms support data marts?

Snowflake, Amazon Redshift, Google BigQuery and Azure Synapse can all support data mart architectures through schemas, datasets, views, access controls and compute separation.

What are the disadvantages of data marts?

Poorly governed data marts can create duplicated data, inconsistent metrics, maintenance overhead and data silos.