What is data virtualization? Data virtualization (DV) is a type of data management in which an application can access and manipulate data without necessarily having any technical details about the data. Data from various sources is aggregated in order to create a single version of the data while leaving the original data in source systems. Information such as how the data was formatted or where it is located is not necessary in order to present the data.
Traditionally, a process known as ETL (extract, transform, load) is used in order to copy data into a destination system. But with data virtualization, the data remains in one place and instead, users are given real-time access to its source system. This way there is a lower risk of data errors, and no single data model is imposed on the data. Data virtualization enables users to access, manipulate, and deliver data more quickly and more cost-effectively than with ETL. Data can be accessed from traditional databases, big data sources, cloud and IoT (internet of things) systems.
Data virtualization performs ETL virtually, instead of using transformation engines, in a process that consists of “connect, combine, consume.” First it connects to the data source, then it combines the information into business views (regardless of their data forma), and finally, it enables users to consume the data via dashboards, reports, mobile apps, or web applications.
Data is generally transferred in different formats and methods and at speeds. Data virtualization allows for the collection, searching, and integration of data from various sources, so that users are able to integrate all the materials into one single model. Since the data is left at the source and accessed virtually rather than being transferred, this helps mitigate security challenges as well as saving money.
The benefits of data virtualization include:
• Zero Replication - An integrated view of the data is drawn from multiple sources without having to move or replicate it. This means fewer redundant copies and a reduction of storage footprints.
• Abstraction - Data can be accessed without its configuration or location information.
• Real-time access to the latest version of data.
• Agility - Data virtualization facilitates a universally semantic layer, which eliminates any enterprise computing disruptions.
• Logical abstraction and decoupling - Distinct data sources, middleware, and platform-specific consumer applications and their formats, interfaces, schema, query paradigms, and security protocols, are connected
• Semantic integration of data - A schema-based approach bridges the semantic understanding of web data and unstructured data.
• Provisioning of agile data services through different formats protocols than the original.
• Unified data security and governance - All data is cohesive and discoverable through a single virtual layer, exposing quality and redundancy issues and achieving consistent integration.
Datacenter virtualization is the process of transforming physical data centers hosted on servers into virtual data centers that use cloud computing technology.
In the past, organizations had to use data, file, and email servers in order to keep up with data processing and storage demands. This led to excessive operating costs and inefficiencies. By virtualizing data centers, multiple applications and operating systems can be run on a single server in the cloud, which greatly improves efficiency, allowing organizations to handle their entire IT framework collectively, often from a single central interface.
Datacenter virtualization used to be used as a tool for developing and testing server environments. Today it enables the delivery of huge amounts of diverse information to users when and as they need it.
Datacenter virtualization generally uses cloud computing technology along with virtualization software to replace equipment, such as traditional servers, that would be traditionally housed in a physical data center.
The benefits of data center virtualization include:
1. Reduction of operating costs - Hardware can be one of the most expensive assets for an IT budget. When you virtualize a data center, you cut capital expenses by saving on buying and maintaining equipment. It also gives you more flexibility within your budget in terms of spending on operating costs and maintenance.
2. Improvement of application performance, including agility, flexibility, performance, and responsiveness, and alleviation of bottlenecks.
3. Minimization/elimination of downtime - Downtime can cost $100,000 or more per hour. The two keys to preventing downtime are:
a. A business continuity plan geared toward minimizing business disruptions. When data centers are virtualized, you don’t need to worry about server hardware failure, which causes a major business disruption. Instead, your IT team can perform upgrades and server maintenance without scheduling in any downtime. In addition, overloaded virtual machines can be migrated across several servers in order to better balance workloads and reduce disruptions
b. A disaster recovery strategy designed to reinstate your company’s operations in the event of data loss from a fire, a flood, a virus, an employee mistake, or a server failure. A virtualized data center is easier to back up than physical hardware servers, and will allow you to get up and running again more quickly.
4. Lowering of heat buildup - Using less physical hardware means less heat production, which prevents equipment failure and shutdown and keeps your data safe.
5. Savings of staff resources and time, allowing your employees to focus on other important IT and business issues.
Data center consolidation encompasses strategies and technologies that enable IT architectures to be more efficient. This can be done by consolidating several data centers into one or by making one specific data center run more efficiently on fewer resources. Data center virtualization can assist in this process by increasing IT flexibility, scalability, and agility while at the same time saving a significant amount of money. When data centers are virtualized, workloads can be deployed faster, performance increases, and operations can become automated, which results in IT that is easier to manage and costs less to operate.
