Data Mart

Data marts are data warehouses which have been designed to meet the needs of specific departments within an organization. Data marts store a selected set of data, which has been extracted and processed from the main warehouse or the source systems.

The data warehouse stores forty terabytes of data. You need the spending from the previous campaign by channel. It involves querying six tables, takes thirty-five minutes, and fails due to timeout. This is the issue that a data mart solves.

However, here is something that most definitions leave out: a data mart does not always solve the problem. There might be cases where using a materialized view within your current data warehouse will do the trick in just two days of engineering, rather than six weeks of building a data mart.

What Is a Data Mart?

Imagine your data warehouse as a hospital where every department has its own wing, staff, and equipment. The data mart is just one of those departments. The data mart contains the information that is relevant to that department alone, without any other department’s data being involved in the query.

The father of dimensional modeling, Ralph Kimball, believed in creating data marts first, and then creating a data warehouse out of them. But Bill Inmon, the man behind the term “data warehouse,” believed in the exact opposite; he believed in creating the central warehouse first and then dividing it into various data marts based on subjects. This discussion between Kimball and Inmon has been a part of every data team’s strategy since their time. No one won that battle. Most companies are a mix of both, and honestly, both work well when done properly.

There are three basic things about data marts that will never change, no matter what way you choose to create them. First, they are subject-oriented. Second, they serve a specific user community. Third, they only contain processed data.

Data Mart vs. Data Warehouse vs. Data Lake vs. Data Lakehouse

	Data Mart	Data Warehouse	Data Lake	Data Lakehouse
Scope	One department or subject	Entire enterprise	Entire enterprise	Entire enterprise
Data type	Structured, processed	Structured, processed	Raw, unstructured	Raw + structured
Primary users	One team’s analysts	BI teams, executives	Data scientists	Data scientists + analysts
Typical size	Varies by use case	Hundreds of GB to TB+	Petabytes	Petabytes
Update frequency	Batch or near-real-time	Scheduled batch	Continuous ingestion	Continuous ingestion
Best for	Fast departmental reporting	Enterprise-wide analytics	ML, unstructured data	Unified analytics + ML

The lakehouse is the innovation that Databricks introduced with Delta Lake, which applies ACID transactions to cloud object storage by means of a transaction log based on file operations over Parquet files, thereby offering warehouse-level query performance without segregating your data from the rest of the data into a structured layer. By 2025, Apache Iceberg had become the de facto standard for table formats, with most data platforms aligning themselves with it, while Delta Lake continued to thrive within the Databricks environment, and the two technologies were now being used together via interoperability rather than one replacing the other. For those who must choose between a data mart and a lakehouse in 2026: if your team is mostly using SQL queries to run reports against a defined subject area, the data mart will remain simpler and cheaper to implement.

The 3 Types of Data Mart

Dependent Data Mart

In a dependent data mart, data comes from a centralized data warehouse. This is where data quality, integration, and transformation take place; a data mart will only get selected data that has been cleaned and organized.

Good fit for: companies who already have a mature data warehouse infrastructure and who wish to provide faster access to certain teams without having to build ETL pipelines for each team individually.

However, be careful. This can be a single point of failure for your entire analytical system. If something happens to the data warehouse, everything else will be affected as well.

If you still have an immature data warehouse or have issues with data quality, your data mart will suffer from the same problems.

Independent Data Mart

An independent data mart connects to source systems like transactional databases, SaaS applications, and CSV files directly, skipping the central data warehouse altogether.

Useful for: startups and small businesses that don’t yet have a data warehouse, or an individual team that requires speed that cannot be achieved by the central data team.

Consistency is where the pain starts for teams down the road. If the sales team’s data mart considers active customers as people who made a purchase in the past 30 days, while the finance team’s data mart considers an active customer as a person who hasn’t canceled a subscription, you will get two different numbers for the exact metric on the same day.

Hybrid Data Mart

The hybrid mart combines data from a central warehouse as well as direct source feeds. The core historical data will be sourced from the warehouse while the new or experimental data will be provided by the direct source feeds.

For instance, in case of a retail firm setting up a loyalty program, the existing transactions will be sourced from the warehouse while the real-time data on loyalty redemptions will be fed directly from the loyalty platform. Once the program stabilizes and the data model is proven, the direct feed is migrated to the warehouse.

The hybrid mart should be considered an interim architecture.

Data Mart Structures Explained

Star Schema

Data marts operate quickly because of the star schema.

One fact table exists at the core; this table has all the measurable events you track, such as sales, page views, or tickets. Dimension tables surround it – time, product, geographic region, customer. If the fact table tracks sales, it will have Sale ID, Product ID, Region ID, Date ID, and Revenue Amount fields. The Product dimension table will contain Product ID, Name, Category, and Unit Cost. In case an analyst wants to analyze revenue per product category in Q1, the system will join the two tables only once.

Dimension tables in the star schema are denormalized; this means there is redundant information in them, but not the costly join chain. In most cases involving data marts, this decision is correct. Speed of queries outweighs the extra cost.

Snowflake Schema

The snowflake schema normalization applies to the dimension tables. You would no longer store Product Category in the Product dimension table but have another table called Category and join that. Less data redundancy; more joins per query.

With computing costs increasing and decreasing elastically with the cloud, and inexpensive storage available, there are few occasions when the space savings from using the snowflake schema are enough to make up for the hit on query performance. Modern query optimizers built for cloud platforms such as Snowflake and BigQuery have helped to close that gap a lot from traditional on-prem systems.Avoid snowflake for simple reporting needs. Use it if you have a complex hierarchy with many levels.

Vault Schema

The one aspect of data warehouses that tutorials fail to address is the concept of data vault, which is definitely not a good idea if you’re designing an enterprise-level data warehouse.

A vault schema breaks down the raw data into hubs (entities such as Customer or Product), links (relationships between entities), and satellites (attributes and history of each entity). The vault design is especially made to be resistant to changes – if you need to accommodate some changes in your source systems, you’ll add new satellites to the vault schema without modifying existing tables. In contrast, a star schema would likely require you to rebuild the schema after such a change.

So, if you’re dealing with complex environments that feature constantly changing source systems, then a vault schema will make your data mart more robust compared to the star schema, but it will cost you a lot of time in year two to re-engineer it.

How a Data Mart Works — ETL vs. ELT

Data can flow into a data mart through two channels.

ETL – Extract, Transform, Load: Data is extracted from source systems, transformed into the correct form in another layer, and finally loaded into the data mart, all pre-cleaned. This has been the conventional method for most of the 2010s.

ELT – Extract, Load, Transform: Data is extracted and directly loaded into the storage layer, where transformations are performed using SQL queries. This is the prevalent method used by cloud platforms currently.

The key to why ELT succeeded over ETL was dbt – data build tool. dbt became the standard transformation layer for cloud data marts due to its capability of letting analytics engineers write transformation logic as SQL models that could be version controlled. Your code sits in Git, gets reviewed in pull requests, and executes test cases automatically. When you design a data mart in 2026 without dbt in mind as your transformation layer, you’re setting yourself up for a harder challenge than required.

ETL was an effective methodology when compute were expensive. ELT becomes relevant when storage is cheap and compute is scalable.

Who Uses Data Marts — Real Use Cases by Department

Marketing: Campaign Attribution. The marketing data mart’s question answered: “What were the drivers of conversions last month and how much did each channel cost per conversion?” In the absence of the data mart, this question requires pulling from five tables, spanning three different sources, and takes an hour of the analyst’s time. Using the marketing data mart, it becomes a ten second pull on a dashboard. The data mart doesn’t have its value in the data but in the time saved every week.

Finance: Profit & Loss by Product Line and Budget vs. Actuals by Cost Center. Finance groups reconcile data from ERP systems, billing platforms, and payroll tools. A data mart built from the three different sources allows the finance team to reduce reconciliation efforts to one schema. Month end closing becomes significantly faster, and fewer mistakes occur from manual spreadsheets consolidation.

Sales: Pipeline Velocity and Quota Achievement by Region. The sales operation data mart that pulls from Salesforce, integrates product data, and pre-calculates all the derived metrics, such as average deal size, days to close, and win rates by segment, allows managers to have their figures ready to discuss at the Monday morning standup meeting.

Operations/Supply Chain: inventory turns, supplier lead times, stockouts. The Supply Chain Mart addresses one critical question above all else: Where will we fall short, and when? This answer, delivered two days earlier than necessary, is the key to proactive vs. reactive ordering.

Each of these begins with a very concrete business question. The mart must follow, not precede, the question.

The Modern Data Mart Tooling Landscape (2026)

Snowflake: pay-per-query computing where the storage is decoupled from processing. The data sharing capabilities of Snowflake enable departments to share their data marts on a single platform without having to set up a pipeline. Due to the strict governance capabilities offered, it becomes a natural choice for teams requiring cross-department access to data with proper permissions.

Google BigQuery: serverless computing, fully managed, and priced per byte scanned. BigQuery BI Engine is a new layer that helps speed up dashboards by querying directly on the mart data. This option works best for teams already working within the Google Cloud environment.

Amazon Redshift: the current enterprise option. With Redshift Serverless, Amazon has taken away most of the complexities involved in managing the clusters.

Databricks: lakehouse platform, although it is becoming increasingly common to use it for data marts using Databricks SQL and Delta Lake tables. If your team does machine learning experiments and SQL reporting simultaneously, then Databricks combines both into one platform.

All four support dbt natively. This is significant because your transformation code can be migrated between platforms while retaining its portability.

Platform choice is not that relevant compared to model choice. A poorly optimized star schema on Snowflake will underperform a well-optimized one on a three-year-old Redshift cluster.

Should You Build a Data Mart? A Decision Framework

Most articles on this topic get the framing exactly wrong. They present data marts as a universal upgrade. They are not. Here is the honest decision.

Build a data mart when:

• The particular team performs the same complicated queries every day and waiting time is a genuine productivity issue
• You require departmental access controls which your warehouse cannot provide
• The team’s analysis requirements are steady enough to warrant designing a schema
• You have the engineering resources for managing a separate data layer

Do not build a data mart when:

• Your data warehouse is not yet stable — a dependent data mart inherits all of its issues
• The materialized view or warehouse view resolves the performance problem of queries within two days of effort
• There are fewer than five individuals querying the same subject area on a regular basis; the overhead is not justified
• You are adopting a data mesh approach — both data mesh and the data mart have similar functionalities, and developing a data mart will only require you to tear it down in eighteen months [over 60% of large organizations are already implementing data mesh, according to dbt Labs’ State of Analytics Engineering 2024]
• Data quality is the major issue here, not data access; a data mart cannot improve poor-quality data in the source system, it simply improves the speed of accessing that poor-quality data

The most expensive data mart ever built is the one that solved a problem a database view could have fixed in an afternoon.

Common Mistakes When Building a Data Mart

Building ahead of a stable warehouse. A dependency on a flawed warehouse means inheriting all of its data quality problems. Those teams who build their mart too quickly without a stable warehouse spend months solving upstream problems rather than adding value.

Over-scope in the first mart. “Marketing data mart” is too broad. “Campaign performance data mart” is the proper scope. Start by building a single topic area, prove the concept, and scale from there. Building for an entire department on the first attempt almost always results in a mart that is too difficult to support.

Forgetting about refresh frequency right out of the gate. How old does the data get before it’s no longer relevant? Can the team handle waiting 24 hours? Then a daily job suffices. Do they need to know how the pipeline is doing in real time? That’s an entirely different solution and should have been considered prior to designing the schema.

Metric definition agreement skipped. What constitutes a “conversion”? What qualifies as an “order completion”? Definitions for such metrics need to be defined and documented even before any table is designed. Data marts constructed without the proper documentation of metric definitions yield untrustworthy figures because everyone has their own interpretation of what goes into the field.

Materialized view used where a regular view is enough. A materialized view in your existing data warehouse provides smart-level performance for most use cases without having a dedicated data layer at all. Test it out first. Every time.

FAQ

Closing

All of the failed data marts that I’ve seen have had one thing in common: there was agreement on the design of the data mart but no agreement on the definitions.

What is meant by “revenue” in your data mart? Is it realized revenue, booked revenue, or potential revenue? And how is an “active” customer defined? By logins, transactions, or 

non-canceled subscriptions?

Defining what the data means should come first.

Designing the tables for the data mart is easy by comparison.