A Comprehensive Guide to Data Virtualization for Enterprises
Enterprises are aggressively investigating beyond the capabilities of traditional data integration such as Extract Transform Load (ETL) systems or data warehouse software as they acquire large volumes of diverse data from an increasing number of sources. Here is a comprehensive guide to data virtualization for enterprises.
Businesses are deploying data virtualization technology solutions to meet increasing data demand for multiple purposes ranging from faster provisioning of new data to enabling self-service data access to clients. It is proving tremendously helpful to data consumers, IT, and technical teams.
Data virtualization is a mature technology currently used as a part of a company’s data integration strategy. According to MarketsandMarkets, the data virtualization market size is expected to grow to USD 1.58 billion in 2017. Furthermore, it is projected to reach USD 4.12 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 21.1% during the forecast period (2017 to 2022).
Data virtualization technology creates a logical extraction layer in distributed data management processing. It allows users to access data of any format and heterogeneous source (data warehouse or data lake) in a standardized manner.
As a result, the users of the data do not need to deal with the technical aspects of data, such as where and how the data is stored, the type of data and its storage structure, and the interface of the original source of data storage, etc.
Further, this data is consumed through virtual views by applications, query/reporting tools, message-oriented middleware, or other data management infrastructure components.
Enterprises can easily access the data they require with data virtualization. A three-step process is involved in the implementation of data virtualization:
Connect: Data virtualization connects to varied data sources, i.e., databases, data warehouses, cloud applications, big data repositories, and even Excel files.
Combine: Data virtualization combines and transforms the related information or date of any format into business views or insights.
Deliver: Data virtualization accesses and delivers real-time data through reports, dashboards, portals, mobile apps, and Web applications to enterprises
While data virtualization technology combines various data sources in a single user interface, the virtual or semantic layer is at the heart of the technology. It allows data or business users to organize their data in different virtual schemas further and virtual views in any format and from any source.
Users can access all unified data from diverse systems through the virtual layer, which produces a single consolidated data source. This information is safe and secure and complies with all industry requirements.
Users can easily enhance this virtualized data to prepare it for analytics, reporting, and automation procedures.
Meets data Demands: As enterprises continue to undertake analysis and employ self-service analytics tools, the data demands of business and data analysts, scientists, and engineers on board might become unmanageable. The findings aid businesses in making better decisions and delighting their customers. As a result, data virtualization allows you to view all your data in real-time from a single, centralized location. This enables the completion of analytics faster than usual.
Manages Data complexity and Volume: The quest for fast expansion has increased the number of unconnected physical databases and complex data in businesses. The quickest way to combine them for analytics is to use data virtualization.
The pace of data generation is clearly increasing, making it more challenging to keep a physical data warehouse up to date. In addition, data virtualization is a more advanced method of transferring data from several locations.
