Introduction to Types of Data Warehouse
Different types of Data Warehouse is nothing but the implementation of a Data Warehouse in various ways such as, namely Data Marts, Enterprise Data Warehouse & Operational Data Stores, which allows the Data Warehouse to be the vital module for Business Intelligence (BI) systems, by performing the process of constructing, managing and performing functional changes on the data from numerous data source that helps in generating reports and Analytical results for significant decision making measures essential for the Business professionals.
Data Warehouse Types
There are three types of data warehouse:
- Enterprise Data Warehouse.
- Operational Data Store.
- Data Mart.
1. Enterprise Data Warehouse
An Enterprise database is a database that brings together varied functional areas of an organization and brings them together in a unified manner. It is a centralized place where all business information from different sources and applications are made available. Once it is stored they can be used for analytics and can be used by all the people across the organization. The data can be classified according to the subject and it gives access as per the necessary division. An Enterprise Datawarehouse will already have the steps of extracting, transforming and conforming already handled.
The goal of EDW is to provide a complete overview of any particular object in the data model. This is accomplished by identifying and wrangling the data from different systems. This is then loaded into a consistent and conformed model. After all the information is gathered by EDW which has the capability of providing access to a single location where different tools can be used to perform analytical functions and create different predictions. The research teams can identify new trends or patterns and focus on them to help the business grow.
Data Marts can be built which make it easier to segregate the data, Relationships between entities can be established and enforced as a part of loading data into EDW. In addition to this slicing and dicing of codes as per different categories can also be done. Also, it helps in reducing costly downtime which may occur due to error-prone configurations with adaptive and machine learning approaches as well. It structures data which helps in operating on a relatively small scale, organization and structure it. The data is stored in a logical and consistent manner.
2. Operational Data Store
As an alternative to having an operational decision support system application an operational data store is used. It helps in accessing data directly from the database which also supports transaction processing. The data which is present in the Operational Data Store can be scrubbed and the redundancy which is present can be checked and resolved by checking the corresponding business rules. It also helps in integrating contrasting data from multiple sources so that business operations, analysis, and reporting can be easily carried out and help the business while the process is still in continuation.
Here most of the operations which are currently being performed are stored before they are moved to the data warehouse for a longer duration. It helps effectively on simple queries and small amounts of data. It acts as a short term or temporary memory which stores the recent information. The data warehouse stores the data for a comparatively long time and also stores relatively permanent information.
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It helps in storing transactional data from one or more production systems and loosely integrates it. It is sometimes subject oriented and time variant. The integration is achieved by making use of EDW structures and contents. The integration of data can involve cleansing, resolving redundancy, checking business rules for integrity. It is usually designed to contain low-level atomic data that stores limited data.
3. Data Mart
Data Mart focuses on storing data for a particular functional area and it contains a subset of data that is stored in a data warehouse. Data Marts help in enhancing user responses and also reduces the volume of data for data analysis. It makes it easier to go ahead with the research. Data Mart being a subset of Datawarehouse is easy to implement. It is cost-effective when compared with a complete data warehouse. It is more open to change, and a single subject matter expert can define its structure and configuration. The data is partitioned, and the granularity can be easily controlled. Data Mart has three types. These types are:
- Dependent
- Independent
- Hybrid
Dependent Data Mart
By getting data from operational, external or both sources a dependent data mart can be created. It allows the sourcing organization’s data from a single data warehouse. All data is centralized and can help in developing more data marts.
Independent Data Mart
This data mart does not require a central data warehouse. This is usually created for smaller groups which are present within an organization. It does not have any relationship with Enterprise Data Warehouse or any other data mart. All data is independent and can be used separately. Also, the analysis can be performed autonomously. To have a consistent and centralized store of data is very important so that multiple users can use it.
Hybrid Data Mart
As the name suggests a hybrid data mart is used when inputs from different sources are a part of a data warehouse. It is useful when a user wants an ad hoc integration. Whenever an organization needs multiple database environments and fast implementation then this setup can be used. It requires the least data cleansing effort and the data mart supports large storage structures. The best usage of a data mart is when smaller data-centric applications are being used.
Conclusion
A data warehouse is thus a very important component in the data industry. As database helps in storing and processing data, a data warehouse helps in analyzing it. Data warehouse thus helps in getting business trends and patterns which can later be presented in the form of reports which provide insight for how to go ahead in the process of business growth. Data warehouse thus plays a vital role in creating a touch base in the data industry.
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