What is Data Mart? Types and Implementation Steps:




What is Data Mart?

In the Data Warehouse system Data Mart is a major role. Data Mart contains a subset of an organization. In other words, a data mart contains only those data that are specific to a particular group. For example, the marketing data mart may contain only data related to items, customers, and sales.

  • Data Marts improve end-user response time by allowing users to have access the specific type of data they need to view most often by providing the data in a way that supports the collective view of a group of users.
  • Data Marts are confined to subjects.

Three Types of Data Mart:

Based on data source dividing the Data Mart into three types
1.Dependent: In contrast, are standalone systems built by drawing data directly from internal or external sources of data or both. This allows you to unite the organization’s data in one data warehouse system with centralization.

2.Independent: Data Mart is created without the use of a central data warehouse for smaller groups within an organization.

  • 3.Hybrid: Data Marts can draw data from operational systems or data warehouses. It is a combination of dependent and independent data warehouse.

Implementing Datamart with simple steps:

Data Mart implementing is a bit of a complex procedure. Here are the detailed steps to implementing the Data Mart:

Designing:

This is the first phase of Data Mart implementation while tasks assigned. At the time gathering the information about the requirements. Then create the physical and logical design of the data mart.

Constructing:

Constructing is the second phase of implementation. It involves creating the physical database and logical structures in data warehouse system. Here are storage management, fast data access, data protection, and security for constructing the database structure.

Populating: 

Populating is the third phase of implementation. It involves Mapping data from source to destination, extraction of source data and loading data into the Data Mart.

Accessing: 

Accessing is the fourth step phase of implementation. It involves querying data, creating reports and charts, etc.

Managing:

Managing is the final step of Data Mart implementation. It involves access to management, tuning the data for the required database and managing fresh data into the data mart.

What is Data Warehouse? Types with Examples

First, we need to basic knowledge on Database then will go with Data Warehouse and different types of Data Warehouse system.



What is Data Base?

Database is a collection of related data and data is a collection of characteristics and figures that can be processed to produce information. Mostly data represents recordable facts. The data aids in producing information, which is based on facts. If we have data about salary obtained by all students, we can then conclude about the highest salary, etc.

What is a Data Warehouse?

Data Warehouse is also an enterprise data warehouse, it is a subject – oriented, integrated, time -variant and non – violent collection of Data management’s decision making.

Data is populated into the Data Warehouse through the processes of extraction, transformation, and loading (ETL). It contributes to future decision making. For example, data will take from different sources is extracted into a single area and transformed according to the data then loaded into storage systems.

Subject Oriented: A data warehouse used to analyze a particular subject area.

Integrated Oriented: A data warehouse integrates data from multiple data sources.

Time-Variant: Historical data is kept in a data warehouse.

Non – Violate: Once data is in the data warehouse, it will not change.

Types of Data Warehouse?

The data warehouse system is majorly three types:

1.Information Processing: A data warehouse allows to process the data stored in it. The data can be processed by means of querying, basic statistical analysis and reporting using charts or graphs

2.Analytical Processing: A data warehouse supports the analytical processing of the information stored in it. Basically, it is OLAP operations including drill – up and drill -down and pivoting.

3.Data Mining: In Data warehouse system Data Mining supports knowledge discovery by finding hidden patterns and associations, constructing analytical models. These results using the visualization tools.