In data warehousing, what is a data mart and how does it relate to an enterprise data warehouse?

Introduction / Context:
Data marts are a key concept in business intelligence architecture. While enterprise data warehouses aim to integrate data across the entire organization, data marts focus on specific departments or subject areas such as sales, finance, or marketing. Interviewers ask about data marts to check whether you understand how analytical solutions can be scoped and delivered incrementally.

Given Data / Assumptions:

• An organization may have an enterprise data warehouse that brings together data from many operational systems.
• Different departments often have unique reporting and analysis needs.
• Not all users require access to the full enterprise data set.
• Performance and security considerations may encourage creating smaller, targeted data structures.

Concept / Approach:
A data mart is a logical or physical subset of a data warehouse that is focused on a single subject area or business function. It usually contains fact and dimension tables relevant to that topic, such as sales measures and related dimensions for a Sales data mart. Data marts can be built directly from source systems (dependent versus independent data marts), but best practice often uses an integrated warehouse as the central source. This ensures consistent definitions of measures and dimensions while allowing tailored solutions for different user groups.

Step-by-Step Solution:
Step 1: Define a data mart as a smaller, focused collection of analytical data aimed at a specific department or subject. Step 2: Explain that typical examples include a Finance data mart, a Marketing data mart, or a Human Resources data mart. Step 3: Describe how a data mart often uses dimensional models (facts and dimensions) related only to that area, improving query performance and usability. Step 4: Clarify that data marts may be physically separate databases, separate schemas, or logical subsets implemented with views on top of an enterprise warehouse. Step 5: Emphasize that data marts complement, rather than replace, an enterprise data warehouse by providing departmental views of shared, consistent data.

Verification / Alternative check:
In many organizations, the main data warehouse feeds downstream data marts that power specific reporting tools or semantic models. Documentation often shows enterprise wide conformed dimensions such as Date and Customer being reused across multiple data marts. Usage statistics typically indicate that departmental analysts work primarily with their own data mart while executives use summary views that may span several marts, confirming the role of data marts as focused subsets of the broader warehouse.

Why Other Options Are Wrong:
Option B confuses a data mart with transaction logs used for recovery, which have very different structures and purposes. Option C suggests data marts are temporary tables created for each query, which describes query execution internals rather than architectural components. Option D misrepresents a data mart as a full enterprise replacement with all data in one small table, which is neither realistic nor aligned with standard BI practices.

Common Pitfalls:
A common pitfall is building many independent data marts directly from source systems without a central integration layer, leading to inconsistent definitions of metrics and dimensions. Another mistake is failing to govern which data should be included in a data mart, resulting in uncontrolled growth and duplication. Effective data warehousing strategies use data marts as user friendly, governed slices of an integrated enterprise warehouse.

Final Answer:
A data mart is a smaller, subject oriented subset of a data warehouse that serves the analytical needs of a particular department or business area rather than the entire enterprise at once.