A data mart is a very simple form of a data warehouse. It focuses on a single subject like sales, marketing or finance. Generally, the data marts are built and controlled by a single department within an organization. The data marts usually draw data from only a few sources, as their focus is on a single-subject. These sources could be internal operational systems, external data or a data warehouse.
The data mart is a subset of the data warehouse that is usually oriented to a specific line of business. Data marts are small slices of the data warehouse and the information in data marts belongs to a single department. In some organizations, each department or business unit is considered the owner of its data mart including all the hardware, software and data. This helps each department to use, manipulate and develop their data without altering information of other data marts or the data warehouse.
A data mart is a subject-oriented archive that stores data and uses the retrieved set of information to assist and support the requirements involved within a particular business function or department. Data marts exist within a single organizational data warehouse repository.
Data marts improve end-user response time by allowing users to have access to the specific type of data they need by providing the data in a way that supports the collective view of a group of users.
A data mart is the access layer of the data warehouse environment that is used to get data out to the users. The major goal and use of a data mart is business intelligence (BI) applications. BI is used to gather, store, access and analyze data. The data mart can be used by smaller businesses to utilize the data they have accumulated.
1. Benefits of a Data Mart
The major benefits of data mart are as follows:
- It combines and integrate data from multiple sources.
- The data structures are defined in the terminology of the user g. instructor, course, fund, grant proposal etc.
- Standardize data across the organization: a “single version of the truth”
- It improves the turnaround time for creating reports and developing analyses.
- It enables access using multiple software tools because a standard data structure is used
2. Types of Data Marts
There are three basic types of data marts. These are dependent, independent, and hybrid. This classification is based on the data source that feeds the data mart.
- Dependent Data Marts: The dependent data marts draw data from a central data warehouse that has already been Dependent data marts are usually built to achieve improved performance and availability, better control, and lower telecommunication costs resulting from local access of data relevant to a specific department.
- Independent Data Marts: Independent data marts, in contrast, are standalone systems built by drawing data directly from operational or external sources of data, or The creation of independent data marts is often driven by the need to have a solution within a shorter time.
- Hybrid Data Marts: Hybrid data marts can draw data from operational systems or data A hybrid data mart allows you to combine input from sources other than a data warehouse. This could be useful for many situations, especially when you need ad hoc integration, such as after a new group or product is added to the organization. Figure 13.4 illustrates a hybrid data mart.
3. Steps to Implement a Data Mart
There are five main steps to implement a data mart. These are as follows: to design the schema, to construct the physical storage, to populate the data mart with data from source systems, to access it to make informed decisions, and to manage it over time. These are discussed as follows briefly.
- Designing: The design step is first in the data mart 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
- 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 This step involves the following tasks:
- Creating the physical database and storage structures, such as table spaces, 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.
- 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 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.
- Accessing: The accessing step involves putting the data to use: querying the data, analyzing it, creating reports, charts, and graphs, and publishing 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 This layer, the meta layer, 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.
- Managing: This step involves managing the data mart over its 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.
4. How Data Mart is 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. Generally, a data warehouse collects data from multiple source systems.
Data marts are small slices of the data warehouse. The data warehouses have an enterprise-wide depth while the information in data marts pertains to a single department. A data mart can be less expensive than implementing a data warehouse, thus making it more practical for the small business. A data mart can be set up in much less time than a data warehouse.
The data marts are generally smaller and less complex than data warehouses. This means they are easier to build and maintain. The followings are the major differences between a data warehouse and a data mart.
Source: Gupta Satinder Bal, Mittal Aditya (2017), Introduction to Basic Database Management System, 2nd Edition-University Science Press (2017)