Title: Data Mart Author: Kriko Published: Jun 4, 2023 Last modified: Jul 7, 2026 --- 1. [Home](https://kriko.io/) / 2. [Glossary](https://kriko.io/glossary) / 3. D Letter # Data Mart A **[data](https://kriko.io/services/data-analysis-insight) mart** is a [data](https://kriko.io/services/data-analysis-insight) 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](https://kriko.io/services/data-analysis-insight) warehouse. While [data](https://kriko.io/services/data-analysis-insight) warehouses collect large volumes of information from different departments and systems within a central environment, [data](https://kriko.io/services/data-analysis-insight) marts focus on smaller and more specialised datasets. As a result, they are often easier to manage, query and analyse. [Data](https://kriko.io/services/data-analysis-insight) 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](https://kriko.io/services/data-analysis-insight) mart may focus on sales revenue, order volume, product performance, customer segments and regional results. A marketing [data](https://kriko.io/services/data-analysis-insight) mart may include campaign performance, [conversion rates](https://kriko.io/services/conversion-rate-optimization), customer acquisition costs and channel-level results. One of the main advantages of [data](https://kriko.io/services/data-analysis-insight) 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](https://kriko.io/services/data-analysis-insight) 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**. ## Discover it in the dictionary ### Bot (Crawler) A bot generally refers to software that performs specific tasks automatically on the internet. 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