Data Virtualization: Working, Advantages, & Disadvantages Explained

Data virtualization is a data management approach that allows applications to access and manipulate data without needing detailed information about its physical storage or format. It integrates real-time data from various sources such as databases, data warehouses, and spreadsheets without replication, creating virtual views for easier access and analysis. The process employs a three-layer architecture—Connection, Abstraction, and Consumption—to consolidate and deliver data seamlessly. Advantages of data virtualization include modernizing data infrastructure, reducing resource needs, enhancing security, and providing flexible, quicker data access, while its disadvantages include potential single points of failure and high initial costs. Common applications of data virtualization span industries like finance and healthcare, supporting business intelligence, real-time analytics, and agile BI by merging disparate data sources into a cohesive view. Despite initial setup challenges, it offers significant benefits in cost savings, productivity, and data management flexibility, facilitated by tools such as Denodo Platform and IBM Cloud Pak for Data.

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