Title: Data Mart
Author: Kriko
Published: Jun 4, 2023
Last modified: Jul 7, 2026

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# Data Mart

A **data mart** is a data storage structure designed to meet the information and
analytical needs of a specific department or business function within a company 
or organisation. It is commonly used as a subject-focused subset of a larger data
warehouse.

While data warehouses collect large volumes of information from different departments
and systems within a central environment, data marts focus on smaller and more specialised
datasets. As a result, they are often easier to manage, query and analyse.

Data marts can be designed for specific functions such as sales, marketing, finance,
human resources and customer relations. Each structure contains the metrics, reports
and datasets required by its intended users.

For example, a sales data mart may focus on sales revenue, order volume, product
performance, customer segments and regional results. A marketing data mart may include
campaign performance, conversion rates, customer acquisition costs and channel-level
results.

One of the main advantages of data marts is that they allow users to access relevant
information more quickly. Smaller, subject-oriented datasets can improve query performance
and simplify reporting processes.

Data marts also help organisations address department-specific information requirements.
Users can access relevant data without searching through large volumes of information
that are unrelated to their responsibilities.

A data mart may be designed as dependent, independent or hybrid. Dependent data 
marts receive data from a central data warehouse, while independent data marts collect
information directly from operational systems. Hybrid data marts can combine data
from both sources.

The main **data modelling schemas** used in data marts include:

**Star Schema:** This structure contains a central fact table that stores measurements
such as sales, transactions or orders. The fact table is connected to dimension 
tables containing descriptive information such as customer, product, date and location.
Its relatively simple structure supports fast querying and reporting.

**Snowflake Schema:** This structure is created by dividing the dimension tables
used in a star schema into smaller and related sub-tables. It can reduce data duplication
and support the management of more complex hierarchies. However, the increased number
of tables may make queries more complicated.

Data can be transferred into a data mart using **ETL and ELT tools**, data integration
platforms and business intelligence solutions. These tools simplify the extraction,
transformation and loading of data from different sources into the target data mart.

A properly designed data mart can help departments create reports more quickly, 
monitor performance indicators and make **data-driven decisions**.

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