Introduction to Data Warehouse Features
Data Warehouse Features can be defined as a group of characteristics that can explain the benefits or drawbacks of the Data Warehousing concept. These characteristics typically describe the various functional attributes of the Data Warehouse system, a central repository to store data from various data sources. These features are the reasons why organizations choose to create and install the Data Warehouse systems onto their data management structures. It makes the analysis and decision-making processes effortless for the business professionals and other leaders in an organization, as even one bad decision can lead to a series of mishaps in the organization’s progress towards success.
Features of Data Warehouse
Below are the different features of Data Warehousing techniques, based on which the Database professionals can choose to implement this system in their organization,
1. Integrated
The Data Warehouse systems are known for their integration ability. The integration concept here can be explained as placing a general entity to filter and capture the like items from the unlike items in multiple database systems. These like data sets are then moved to the data warehouse, using a general principle or a common format. This method is followed in all the data warehouse systems, as it aids in keeping the integrity of the data in the data warehouse to be intact.
Normally, a data warehouse is formulated by incorporating the data and information from diverse data sources connected and operated for a specific set of application systems. These data sources can have a distinct data format, or in most cases, can have a mix of data formats, such as the documents, numbers, files of different formats, characters with or without symbols, etc. The data sources are also subjected to various update processes like common naming standards, data formatting, sorting, etc. The process of integrating the data facilitates the successful examination of the data from the database systems, which can be kept ready for later to be used for any kind of operation. The readiness of the data can improve the system performance and decrease the processing time with remarkable progress in analytical flow. Clean and common formatted data is the outcome of the successfully integrated data in a Data Warehouse system.
2. Time-Variant
Time Variance is nothing but the time prospect of the data warehouse system, which is observed to be fairly widespread in nature. It is generally adaptive to the time and date set on the operational systems, as the time can be directly reflecting on the data warehouse systems. The data and contents of the data warehouse is documented under a specific period of time, which is then put forward with the information gathered from the previously-stored prospects. Every data unit inside the data warehouse databases can hold the factor of time, both plainly and in a formatted manner.
The data in the Data warehouse will usually have the time indicators along with it, as and when placed in the tables of the database. The databases are designed so that every record stored in the tables inside the database is accommodated with the time unit along with them, where the time unit is of a specific format with date & time. This time of each data is updated whenever there is a change made to the data. When a person in a different time zone accesses the data, the time conversion is automatically carried out with respect to the system time of that person.
3. Subject-Oriented
The term Subject –Oriented can be explained as a phenomenon where the data stored in the database systems are based on a specific topic. It can also mean that the data in the system are grouped under a common idea, which can be used for keeping the focus in place inside the Data warehouse. Grouping is an important characteristic of the Data Warehouse, which aids in keeping the system organized and well –organized. The usual subjects for this grouping mechanism depend on the industry or the organization’s market. A few examples of the subjects are employees, marketing, sales, research, products, customers, etc.
The Database organization in a Data Warehouse is not focused on the current or upcoming actions carried out on the Data Warehouse system. On the contrary, the load is shared amongst various processes like the design, implementation, analysis, and maintenance of the database systems, from which the required data are pulled out for the decision-making progression. This is used for creating an easy, precise, and terse outlook for a definite matter, which is achieved by not including the unsupported data for any decision-making activity.
4. Non-volatile
The Non –Volatility of the Data warehouse is a commonly known characteristic, as its data in the databases are not permanently cleared when a user deletes it. Instead, the deleted data is moved to a recyclable location, which can be retrieved back as and when required. In most cases, the Data in the database systems are non-editable and refreshed on a scheduled time frame. The process of tracking the activities performed on each data can let the system store the history of the data present in the database, and so when the user needs to know the previous actions, he/she can easily fetch the data’s history.
This helps in combining multiple data control methods into a single history maintenance activity. Additionally, the database doesn’t require manipulative operations like inserting, updating, and deleting the data in a given specific background. On the other hand, the data warehouse can be subjected to two different operations that can be applied to the data placed in the databases of the Data Warehouse system, as below,
- Data Loading
- Data Access
Conclusion
Data Warehouse Features are why an organization picks the idea of implementing the Data Warehousing methodology to make important decisions that can help the business professionals achieve their goals. When the business information is well handled with the help of the Data Warehouse, the results can be used for storing historic information and creating analytical results in the form of reports.
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This is a guide to Data Warehouse Features. Here we discuss the different features of Data Warehousing techniques in detail. You may also have a look at the following articles to learn more –
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