What is Data Mart? Definition & Concept

#Wikipedia
A data mart is the access layer of the data warehouse environment that is used to get data out to the users. The data mart is a subset of the data warehouse that is usually oriented to a specific business line or team. Data marts are small slices of the data warehouse. Whereas data warehouses have an enterprise-wide depth, the information in data marts pertains to a single department. In some deployments, each department or business unit is considered the owner of its data mart including all the hardware, software and data. This enables each department to use, manipulate and develop their data any way they see fit; without altering information inside other data marts or the data warehouse.

#MicroStrategy

A datamart is a mini data warehouse, or a subset of data derived from a primary data warehouse. Datamarts are typically stored in the same database server as the warehouse. A data mart is a relational table containing results of a report.

#DataMartist
Data Warehouse :

• Holds multiple subject areas
• Holds very detailed information
• Works to integrate all data sources
• Does not necessarily use a dimensional model but feeds dimensional models

Data Mart :

• Often holds only one subject area- for example, Finance, or Sales
• May hold more summarized data (although many hold full detail)
• Concentrates on integrating information from a given subject area or set of source systems
• Is built focused on a dimensional model using a star schema

#Webopedia
A database, or collection of databases, designed to help managers make strategic decisions about their business. Whereas a data warehouse combines databases across an entire enterprise, data marts are usually smaller and focus on a particular subject or department. Some data marts, called dependent data marts, are subsets of larger data warehouses.
 

#Oracle

What Is a Data Mart?

A data mart is a simple form of a data warehouse that is focused on a single subject (or functional area), such as Sales, Finance, or Marketing. Data marts are often built and controlled by a single department within an organization. Given their single-subject focus, data marts usually draw data from only a few sources. The sources could be internal operational systems, a central data warehouse, or external data.

How Is It Different from a Data Warehouse?

A data warehouse, unlike a data mart, deals with multiple subject areas and is typically implemented and controlled by a central organizational unit such as the corporate Information Technology (IT) group. Often, it is called a central or enterprise data warehouse. Typically, a data warehouse assembles data from multiple source systems.

Nothing in these basic definitions limits the size of a data mart or the complexity of the decision-support data that it contains. Nevertheless, data marts are typically smaller and less complex than data warehouses; hence, they are typically easier to build and maintain. Table A-1 summarizes the basic differences between a data warehouse and a data mart.

What Are the Steps in Implementing a Data Mart?

1. Designing
The design step is first in the data mart process. This step covers all of the tasks from initiating the request for a data mart through gathering information about the requirements, and developing the logical and physical design of the data mart. The design step involves the following tasks:

• Gathering the business and technical requirements
• Identifying data sources
• Selecting the appropriate subset of data
• Designing the logical and physical structure of the data mart

2. Constructing
This step includes creating the physical database and the logical structures associated with the data mart to provide fast and efficient access to the data. This step involves the following tasks:

• Creating the physical database and storage structures, such as tablespaces, associated with the data mart
• Creating the schema objects, such as tables and indexes defined in the design step
• Determining how best to set up the tables and the access structures

3. Populating
The populating step covers all of the tasks related to getting the data from the source, cleaning it up, modifying it to the right format and level of detail, and moving it into the data mart. More formally stated, the populating step involves the following tasks:

• Mapping data sources to target data structures
• Extracting data
• Cleansing and transforming the data
• Loading data into the data mart
• Creating and storing metadata

4. Accessing
The accessing step involves putting the data to use: querying the data, analyzing it, creating reports, charts, and graphs, and publishing these. Typically, the end user uses a graphical front-end tool to submit queries to the database and display the results of the queries. The accessing step requires that you perform the following tasks:

• Set up an intermediate layer for the front-end tool to use. This layer, the metalayer, translates database structures and object names into business terms, so that the end user can interact with the data mart using terms that relate to the business function.
• Maintain and manage these business interfaces.
• Set up and manage database structures, like summarized tables, that help queries submitted through the front-end tool execute quickly and efficiently.

5. Managing
This step involves managing the data mart over its lifetime. In this step, you perform management tasks such as the following:

• Providing secure access to the data
• Managing the growth of the data
• Optimizing the system for better performance
• Ensuring the availability of data even with system failures

For original and detail documentation please see here...