Data virtualization is a way of representing data in a computer system that is not physically stored in that system. It is a process of creating a virtual version of something, including data, applications, or entire IT systems. The technology provides a layer of abstraction between the physical data stores and the users who need to access them. This enables business users to gain timely insights into their operations without having to wait for IT to pull together the necessary data from various siloed systems.
Keep reading to learn more about data virtualization, how it works, and how it can benefit your business.
What is data virtualization architecture?
Data virtualization architecture is designed to manage the complexities inherent in consolidating data from multiple sources. It consists of three key components: a broker, providers, and consumers. The broker sits between the providers and consumers, managing communication between them and translating requests as needed. The providers are responsible for supplying the relevant data, while the consumers use it to generate reports or perform other analyses. One of the benefits of data virtualization is that it allows businesses to consolidate information from different systems into a single view. This can provide insights that would not be possible if the data were examined in isolation. Additionally, by consolidating data into a central repository, businesses can reduce duplication and save storage space. Data virtualization also makes it easier to comply with regulatory requirements since all relevant information is housed in one place.
What are the mechanisms of data virtualization?
There are three primary mechanisms of data virtualization:
1) Data federation: This mechanism involves integrating data from multiple disparate data sources into a single virtual data store. This can be achieved through a variety of methods, such as data replication, data synchronization, data aggregation, and data transformation.
2) Data transformation: This mechanism involves transforming the data from one format to another in order to make it compatible with the virtual data store. This can be done through a variety of methods, such as data scraping, data parsing, and data cleansing.
3) Data abstraction: This mechanism involves hiding the details of the underlying data sources from the users of the virtual data store. This can be done through a variety of methods, such as data virtualization engines, data mappers, and data adapters.
What are the general benefits of data virtualization?
As mentioned, data virtualization is the process of creating a logical view of data that does not exist in its physical form. This can be done by consolidating data from multiple data sources into one place, or by creating a copy of the data that is accessible to multiple users at the same time. Data virtualization makes it possible to access and use information from different data sources without having to physically move the data. It also speeds up the process of retrieving information by eliminating the need to search through multiple databases. In addition, data virtualization can help improve security by allowing companies to control who has access to which parts of the database.
How can data virtualization improve your business?
Data virtualization allows you to access data from various sources, including both internal and external data stores, as if it were all one big store. This can improve your business in a few ways. First, data virtualization makes it easier for you to combine data from different sources into a single report or analysis. This can help you get a more accurate view of your business. Second, data virtualization can make it easier for you to share data with other departments or employees. This can improve communication and collaboration within your company. Finally, data virtualization can make it easier for you to access historical data. This can help you track trends and make better decisions based on past performance.
What are the types of data virtualization?
Data virtualization can be done for reporting and analysis purposes, or to improve the performance of data-intensive applications.
There are several different types of data virtualization:
Physical Data Virtualization: Also known as “data federation,” physical data virtualization is a process that links together multiple physical databases so that they appear as a single database. This is done by creating links between the tables in each database, and then populating those tables with the relevant data from each source. Physical data virtualization can be used for reporting and analysis, or to improve the performance of applications that need to access multiple sources of data simultaneously.
Logical Data Virtualization: Logical data virtualization (also known as “virtual tables”) creates a logical table out of multiple physical tables. This is done by defining relationships between the tables, and then populating the logical table with relevant data from each source. Logical data virtualization can be used for reporting and analysis, or to improve the performance of applications that need to access multiple sources of logically related data simultaneously.
Composite Data Virtualization: Composite data virtualization combines both physical and logical methods, allowing businesses to create composite views of their data that include both physical and logical entities. This approach provides the greatest flexibility, enabling businesses to reconstruct their data sources any way they want. Composite data visualization can be used for reporting and analysis, or to provide a better user experience when accessing data integrated applications.
What does the future of data virtualization look like?
The future of data virtualization looks very promising. The technology is becoming more and more popular, and vendors are continuing to develop new features and enhancements. In the near future, we can expect data virtualization to become even more pervasive, helping businesses get even better insights from their data.
Overall, data virtualization is an important process that can help improve organizational efficiency and workflow. By consolidating data from various sources into a single location, data virtualization can help reduce the amount of time needed to access and analyze information. This can be especially useful for businesses that rely on data-heavy processes, such as those in the financial or retail sectors.