A Data Mart is focused on a single functional area of an organization and contains a subset of data stored in a Data Warehouse. A Data Mart is a condensed version of Data Warehouse and is designed for use by a specific department, unit or set of users in an organization. E.g., Marketing, Sales, HR or finance. It is often controlled by a single department in an organization. Show Data Mart usually draws data from only a few sources compared to a Data warehouse. Data marts are small in size and are more flexible compared to a Datawarehouse. In this tutorial, you will learn- Why do we need Data Mart?
Types of Data MartThere are three main types of data mart:
Dependent Data MartA dependent data mart allows sourcing organization’s data from a single Data Warehouse. It is one of the data mart example which offers the benefit of centralization. If you need to develop one or more physical data marts, then you need to configure them as dependent data marts. Dependent Data Mart in data warehouse can be built in two different ways. Either where a user can access both the data mart and data warehouse, depending on need, or where access is limited only to the data mart. The second approach is not optimal as it produces sometimes referred to as a data junkyard. In the data junkyard, all data begins with a common source, but they are scrapped, and mostly junked. Dependent Data MartIndependent Data MartAn independent data mart is created without the use of central Data warehouse. This kind of Data Mart is an ideal option for smaller groups within an organization. An independent data mart has neither a relationship with the enterprise data warehouse nor with any other data mart. In Independent data mart, the data is input separately, and its analyses are also performed autonomously. Implementation of independent data marts is antithetical to the motivation for building a data warehouse. First of all, you need a consistent, centralized store of enterprise data which can be analyzed by multiple users with different interests who want widely varying information. Independent Data MartHybrid Data Mart:A hybrid data mart combines input from sources apart from Data warehouse. This could be helpful when you want ad-hoc integration, like after a new group or product is added to the organization. It is the best data mart example suited for multiple database environments and fast implementation turnaround for any organization. It also requires least data cleansing effort. Hybrid Data mart also supports large storage structures, and it is best suited for flexible for smaller data-centric applications. Hybrid Data MartSteps in Implementing a DatamartImplementing a Data Mart is a rewarding but complex procedure. Here are the detailed steps to implement a Data Mart: DesigningDesigning is the first phase of Data Mart implementation. It covers all the tasks between initiating the request for a data mart to gathering information about the requirements. Finally, we create the logical and physical Data Mart design. The design step involves the following tasks:
Data could be partitioned based on following criteria:
Data could be partitioned at the application or DBMS level. Though it is recommended to partition at the Application level as it allows different data models each year with the change in business environment. A simple pen and paper would suffice. Though tools that help you create UML or ER diagram would also append meta data into your logical and physical designs. ConstructingThis is the second phase of implementation. It involves creating the physical database and the logical structures. This step involves the following tasks:
What Products and Technologies Do You Need? You need a relational database management system to construct a data mart. RDBMS have several features that are required for the success of a Data Mart.
Populating:In the third phase, data in populated in the data mart. The populating step involves the following tasks:
What Products and Technologies Do You Need? You accomplish these population tasks using an ETL (Extract Transform Load) Tool. This tool allows you to look at the data sources, perform source-to-target mapping, extract the data, transform, cleanse it, and load it back into the data mart. In the process, the tool also creates some metadata relating to things like where the data came from, how recent it is, what type of changes were made to the data, and what level of summarization was done. AccessingAccessing is a fourth step which involves putting the data to use: querying the data, creating reports, charts, and publishing them. End-user submit queries to the database and display the results of the queries The accessing step needs to perform the following tasks:
What Products and Technologies Do You Need? You can access the data mart using the command line or GUI. GUI is preferred as it can easily generate graphs and is user-friendly compared to the command line. ManagingThis is the last step of Data Mart Implementation process. This step covers management tasks such as-
What Products and Technologies Do You Need? You could use the GUI or command line for data mart management. Best practices for Implementing Data MartsFollowing are the best practices that you need to follow while in the Data Mart Implementation process:
Advantages and Disadvantages of a Data MartAdvantages
Disadvantages
Summary:
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