Data Virtualization – Why is it a necessity for data-driven enterprises

Data Virtualization is a broad spectrum that enables data management to retrieve and manipulate data without much prior knowledge of information technology. The technical knowledge like the process of data formatting from the source, finding the physical location of the database and enabling the customer to view the entire data.

Big data means a larger set of data full of complexity, regulated by traditional ways of data handling. Big Data is the combination of V3s i.e. Volume, variety, and velocity. It is a collection of structured, semi-structured, and unstructured data where data with numerous fields have varied statistical data whereas complex data may lead to finding errors in discovery rates.

Streaming Data is the real-time streaming of data in the continuous progression from different sources. It is a continuous flow of data generation without having access to all the data.

IoT means the Internet of Things. This enables the world to get connected with software, sensors, data analysis, global communication, etc. through technology. It makes exchanging information applications or software an easy task.

A Traditional Data Warehouse is an off-line on-site process of storing data locally in your office or another physical corporate warehouse. In traditional data warehousing hardware, technical assistance and manual running of data are required. It is also called an on-premises data warehouse.

Cloud Data Sources are normally used for loading data from some external sources into the system and this will enable the organization to the database for analysis. There are primarily three types of cloud data sources, which are relational, multidimensional data sources, and dimensionally modeled relational data sources.

The best-suited use cases and patterns for Data Virtualization are as follows:

• Mainstream BI and Data Warehousing: It is a Logical Data Warehouse and offers Virtual Data Marts.
• Big Data Analytics: For Hadoop/NoSQL as an Analytics Sandbox. And for Data Warehouse Offloading.
• Data Discovery/Self-Service: Data Discovery and 'What If' Analytics. Self-Service BI and Reporting.

In the Data Virtualization Deployment Model, the early implications of virtualization take place for deploying multiple server applications on the same system. The model includes fetching information, delivering training to the model, evaluating the performance of the model, and implementing deployment and monitoring.