BI Glossary

Data Mart

What is a Data Mart?

A data mart is a data storage system that contains information specific to an organization’s business unit. It contains a small and selected part of the data that the company stores in a larger storage system, such as a data warehouse or a data lake.

Companies use a data mart to analyze department-specific information more efficiently. It provides summarized data that key stakeholders can use to quickly make informed decisions.

For example, a company might store data from various sources, such as supplier information, orders, sensor data, employee information, and financial records in their data warehouse or data lake.

However, the company stores information relevant to, for instance, the marketing department, such as social media reviews and customer records, in a data mart.

Type of Data Marts:

Dependent data mart:

A data mart that is built from an existing data warehouse, using a top-down approach. This type of data mart ensures data consistency and quality, but can be slow and complex to create and maintain.

Independent data mart:

A data mart that is built from other data sources, such as operational systems or external data providers, using a bottom-up approach. This type of data mart provides data flexibility and speed, but can be prone to data duplication and inconsistency.

Hybrid data mart:

A data mart that is built from a combination of data warehouse and other data sources, using an integrated approach. This type of data mart achieves the optimal balance between data reliability and agility, but can be challenging to design and integrate.

What is the difference between a data mart and a data lake?

A data mart and a data lake are both data storage systems, but they have some key differences in terms of their structure, purpose, and use cases.

Data Mart:

A data mart is a subset of a data warehouse that is designed to serve a specific business function or department. It contains a subset of the data from the data warehouse that is specifically relevant to a particular group of users, such as marketing, finance, or sales. Data marts are typically structured and optimized for specific reporting and analysis purposes, making it easier for users to access and analyze the data they need.

Key characteristics of a data mart:

1. Subject-oriented: Data marts are focused on a specific subject area or business function.

2. Structured: Data marts have a predefined schema and data model, making it easier to query and analyze the data.

3. Summarized: Data in a data mart is typically summarized and aggregated for specific reporting and analysis purposes.

4. Smaller in size: Data marts contain a subset of the data from the data warehouse, making them smaller and more manageable.

Data Lake:

A data lake, on the other hand, is a centralized repository that stores all structured and unstructured data in its raw format. It is designed to store large amounts of data in its native format, without the need for upfront data modeling or transformation. Data lakes are often used as a staging area for data before it is processed and loaded into a data warehouse or data marts.

Key characteristics of a data lake:

Schema-on-read: Data in a data lake is stored in its raw format, and the schema is applied at the time of data retrieval (read).
Unstructured: Data lakes can store a variety of data types, including structured, semi-structured, and unstructured data (e.g., text, images, audio, video).
Low upfront cost: Data lakes are typically built on inexpensive object storage, making them cost-effective for storing large volumes of data.
Flexible: Data lakes are designed to handle a wide variety of data types and formats, making them more flexible than traditional data warehouses.

In summary, a data mart is a structured and optimized subset of data from a data warehouse, designed for specific reporting and analysis purposes within a particular business function or department. A data lake, on the other hand, is a centralized repository that stores all types of data in its raw format, providing a flexible and cost-effective way to store and analyze large volumes of data.

The choice between a data mart and a data lake depends on the specific requirements of an organization, such as the types of data they need to store, the level of data structure required, and the intended use cases for data analysis and reporting.

What is the difference between a data mart and a data warehouse?

The primary differences between a data mart and a data warehouse are:

1. Scope:

Data Warehouse: A data warehouse is an enterprise-wide centralized repository that integrates data from multiple operational systems and sources across an organization. It provides a comprehensive view of an organization’s data.
Data Mart: A data mart is a subset of a data warehouse that focuses on a specific subject area, department, or business function. It contains only the data relevant to a particular group of users or a specific analytical purpose.

2. Data Sources:

Data Warehouse: A data warehouse sources data from various operational systems, external sources, and other data marts within the organization.
Data Mart: A data mart typically sources its data from a data warehouse, although it can also source data directly from operational systems if needed.

3. Data Structure:

Data Warehouse: Data in a data warehouse is typically structured and organized into a standard schema or data model, making it easier to analyze and report on data from multiple sources.
Data Mart: Data in a data mart is often designed and structured specifically for the requirements of a particular department or analytical purpose, making it easier for end-users to understand and use.

4. Data Volume:

Data Warehouse: A data warehouse typically stores a larger volume of historical and current data from multiple sources across the organization.
Data Mart: A data mart contains a subset of the data from the data warehouse, focusing only on the data relevant to a specific group of users or a particular analytical purpose, making it smaller in size.

5. Users:

Data Warehouse: A data warehouse serves a broad range of users across the organization, including analysts, managers, executives, and decision-makers.
Data Mart: A data mart is designed to meet the specific needs of a particular group of users, such as a department or a business function, providing them with the data and tools they need for their specific analytical requirements.

6. Purpose:

Data Warehouse: The primary purpose of a data warehouse is to support enterprise-wide reporting, analysis, and decision-making by integrating data from multiple sources into a single repository.
Data Mart: The purpose of a data mart is to provide a specific group of users with the data and tools they need to perform their analytical tasks more efficiently and effectively, focusing on a particular subject area or business function.

A data warehouse is a centralized repository that integrates data from multiple sources across the organization, while a data mart is a subset of a data warehouse tailored to the specific needs of a particular group of users or analytical purpose.