Introduction
In today's world, the Enterprise data now comes in various formats and is kept in a variety of locations. There are structured and unstructured data, such as rows and columns of data in a standard database, and formats such as logs, emails, and social media content. Databases, log files, CRM, and other programs store Big Data in various forms.
Data Virtualization is used to manage all this structured and unstructured data, which allows for retrieval and manipulation of data without knowing where it is stored or how it is formatted.
Data virtualization combines data from several sources without replicating or moving it, offering users a single virtual layer that spans numerous applications, formats, and physical locations. This means that data can be accessed more quickly and easily.
Let’s learn about Data Virtualization and its use cases in-depth.
Data Virtualization Abilities
Data virtualization is appropriate for usage with Big Data because of its abstraction and federation. It hides the intricacies of Big Data repositories, whether Hadoop or NoSQL, and makes it simple to link data from these stores with data from other parts of the organization. After all, data virtualization serves this role, and Big Data is intrinsically heterogeneous.
Data Virtualization has several abilities:-
1.) Logical Abstraction and Decoupling: Data virtualization allows heterogeneous data sources to communicate more efficiently.
2.) Cost Savings: It is less expensive to keep and manage data than duplicate it and invest resources in converting it to other formats and places.
3.) Data Governance: By centralizing administration, data governance issues can be alleviated, and rules can be applied to all data from a single location.
4.) Bridging Structured and Unstructured Data: Data virtualization can bridge the semantic gap between unstructured and structured data, making integration easier and improving data quality overall.
5.) Increases Productivity: Aside from the aforementioned data bridging, virtualization makes testing and launching data-driven programs easier because integrating data sources takes less time.
Now that we have learned data virtualization capabilities. Let’s learn about data virtualization use cases.