Data warehouses vs. data marts: Key differences

Modern companies deal with mountains of data. But what’s the best place to store it all? If you don’t know the difference, weighing up the pros and cons of data warehouses versus data marts can be enough to make your head spin.

Data warehouses offer big-picture insights, while data marts zoom in on what matters the most for specific teams. Knowing the difference between a data mart and a data warehouse will help you scale your data architecture and align analytics with business needs.

What’s a data warehouse?

A data warehouse stores large amounts of structured data from multiple sources in a single, centralized repository. Companies can then use that data for enterprise-wide analytics and business intelligence. This is extremely useful when making decisions that require input from multiple departments.

Data warehouses often store historical data, making it easy to identify patterns over time. They’re also non-volatile. Translation: The warehouse doesn’t change or delete data once it’s uploaded, making it reliable for historical analysis.

Key components of a data warehouse

• Data sources: Warehouses store data from multiple systems, from databases to apps and customer relationship management (CRM) platforms. These data sources feed the warehouse with raw information that you can then analyze and act on.
• ETL/ELT processes: Extract, transform, and load (ETL) and extract, load, and transform (ELT) pipelines clean, standardize, move, and prepare data for use. These processes turn raw, messy inputs into reliable, analysis-ready information.
• Staging area: A staging area is a temporary holding zone for raw data before transformation. It prevents the warehouse from being clogged up with low-quality information.
• Storage: The storage area is the heart of the data warehouse, where cleaned, structured data lives. Optimized for speed and scalability, it enables fast queries and accurate insights.
• Business intelligence (BI) tools: Business intelligence tools like Tableau or Power BI turn warehouse data into dashboards, reports, and visual stories. They allow teams to make data-driven decisions with confidence.

What’s a data mart?

A data mart is a subset of a data warehouse focused on a particular department or line of business. Unlike much larger data warehouses, data marts have a limited scope. They focus on a specific subject or business function for fast, easy access to business insights.

Because they contain a much smaller volume of data, data marts are simpler to set up and deploy than data warehouses.

Types of data marts

• Dependent data mart: A dependent data mart is created from pre-existing data in a data warehouse through the ETL/ELT processes. Because the data warehouse has already processed the data, you can spin up marts quickly and efficiently. But it’s important to note that if the central data warehouse ever fails, all data marts dependent on it will become unavailable.
• Independent data mart: An independent data mart is a standalone system that doesn’t rely on a data warehouse. This means the business unit can design a bespoke system that meets its specific needs. But because independent marts require their own ETL/ELT processes, they can be more complicated to set up.
• Hybrid data mart: A hybrid data mart model uses independent and dependent data marts at the same time. Business units can use a dependent mart when they need to stick to the organization’s data governance policies, then turn to an independent mart once they get the freedom to collect their own data.

Key differences between data warehouses and data marts

Data warehouses vs. data marts: Advantages and disadvantages

Data warehouses and data marts both have key strengths and potential tradeoffs. The question is how to make an informed decision based on your organization’s roadmap and needs.

Here’s a breakdown of the advantages and disadvantages of both repository options.

Advantages of data warehouses

• Enterprise-wide view: Data warehouses offer organizations a single source of trusted information, aligning teams around consistent, company-wide insights.
• Scalability: It’s simple to increase the size of a data warehouse to meet growing data volumes and other evolving business needs.
• Integration of multiple sources: You can easily unify data from different systems to deliver a complete, connected picture.
• Advanced analytics support: Warehouses allow complex queries, machine learning, and predictive insights for deeper decision-making.

Disadvantages of data warehouses

• High cost: Data warehouses require significant investment into infrastructure, tools, and expertise.
• Long implementation time: Because they’re more complex and difficult to navigate, designing, building, and deploying an effective warehouse takes longer than implementing a data mart.
• Maintenance complexity: Warehouse systems require continuous updates, monitoring, and optimization to stay efficient.
• Potential for outdated information: If you don’t frequently audit and update a warehouse, data can quickly become outdated.

Advantages of data marts

• Faster implementation: Data marts are much quicker to build than data warehouses, making them ideal for shorter-term analytical needs.
• Cost-effectiveness: They offer powerful insights without the heavy expense of a full warehouse.
• Focused insights: Marts deliver a tailored view of data for specific teams or departments.
• Easier management: These systems are much simpler to maintain and update than their larger counterparts, requiring fewer technical resources.

Disadvantages of data marts

• Risk of silos: Separating data into isolated chunks can limit cross-department visibility.
• Limited scope: Data marts focus on specific functions, leaving out the bigger business picture.
• Inconsistent definitions across departments: Relying on data marts comes with the risk of data being defined and interpreted differently by different teams.
• Less scalability: Marts can struggle to handle large or growing data volumes.

Use cases: How to choose the right approach

Your infrastructure, budget, and analytical needs will determine whether a data warehouse, a data mart, or a combination is right for your organization. In some cases, you may also want to consider a data lake — a flexible, scalable storage layer for raw data that supports emerging use cases like AI/ML.

When to use a data warehouse

• Enterprise-wide analytics: Use a data warehouse when you need a unified, organization-wide view of data from across departments and systems. This option gives you consistency and accuracy across all business insights.
• Long-term trend analysis: Warehouses are ideal for identifying patterns and performance shifts over time. That birds-eye view helps leaders make more strategic, data-driven decisions.
• Compliance reporting: Choose a data warehouse when you need secure, auditable records to keep in line with regulatory or industry requirements.

When to use a data mart

• Department-specific dashboards: Use a data mart when teams like marketing, sales, or HR need fast, tailored access to their own data. This smaller-scale option keeps analysis focused and efficient.
• Rapid access to focused insights: When speed matters more than scale, data marts let teams explore insights quickly. They won’t have to wait for large, enterprise-level systems to be built.

When to use a data lake

• High-volume, flexible storage: Data lakes store raw, structured, and unstructured data cost-effectively. They’re ideal for early-stage exploration, log data, or semi-structured sources.
• AI/ML and advanced analytics: If you’re training models or experimenting with large, diverse datasets, data lakes offer the flexibility and scale you need.

When to use a combination

• Hybrid approach: Most modern stacks use all three. For example, you can route raw data to a lake for flexible storage and AI/ML use cases, transform it in a warehouse for reporting, and surface it in data marts for team-level insights.

How Fivetran simplifies data warehouse and data mart workflows

Whether you’re loading data into a centralized warehouse or spinning up a department-specific marts, Fivetran makes it easy to move and manage data at scale. 

Fivetran seamlessly syncs data from multiple sources, ensures schema consistency, and automates ELT pipelines — all with minimal engineering overhead. For data teams, this means:

• Faster deployment of large volumes of data
• Trusted data pipelines that adapt to change
• Integrated real-time insights for business intelligence

And as more organizations adopt modern architectures, Fivetran also supports flexible landing zones like data lakes, enabling AI/ML use cases and unlocking new value from raw data.

Get started for free or book a demo to see how Fivetran can empower your data teams and meet your analytics needs.

FAQs

What are data warehouses and data marts?

Data warehouses are centralized, integrated repositories that consolidate data from multiple sources. Data marts are smaller, department-specific, decentralized collections of data.

What’s the relationship between a data mart and a data warehouse?

A data mart is essentially a smaller data warehouse designed to serve the specific needs of individual teams or departments. While data marts can be independent of data warehouses, they often rely on information stored in the larger warehouse, cordoning off data useful for specific departmental needs.

What’s the difference between a data lake and a data mart?

A data lake stores unprocessed data from multiple sources in its original format, while a data mart contains structured, processed data for targeted analysis. Data lakes are for storage and exploration, while data marts are for fast, business-ready insights.

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