An Explanation Of Virtualization Software In Simple Terms

Organizations rely on multiple data management systems that are interconnected via a data infrastructure.

This infrastructure is comprised of databases, data warehouses, data marts, data lakes, and storing insights. Facilitating data movement and extracting business insights requires a range of data management solutions that are often complex to implement and manage. Data virtualization technology streamlines this process and allows end-users to leverage the operational capabilities of their data infrastructure.

What Is Data Virtualization?

An Explanation Of Virtualization Software In Simple Terms

Data virtualization creates an abstract, virtual layer that gathers business data from disparate sources into a single source of truth without performing the entire Extract-Transform-Load (ETL) process. Virtualization connects disparate data sources, integrates information into a virtual view, and publishes data as a data service. This results in real-time access to accurate data and analytics that improve business intelligence and decision-making.

Creating a virtual environment for pertinent data allows end-users to access specific data without the need for technical details about data location or data source structure. This allows organizations to restrict data access to ensure security and compliance with data governance requirements. Virtualization software streamlines key processes to make data accessible in a single source in specific formats. Reducing the risk of data errors by consolidating data into a single source results in better data quality. Modern data integration breaks down data silos and formats, performs data replication in real time, and allows for greater speed and agility, and a faster response time. Virtualization software aids with data mining, data analytics, predictive analytics, and effectively uses machine learning and artificial intelligence.

Data Virtualization Capabilities

An Explanation Of Virtualization Software In Simple Terms

Data virtualization is ideal for organizations with big data. A single physical server may not be able to handle the workload of big data. Less frequently accessed data is often offloaded from a physical server such as an SSD to a hard disk, or cloud infrastructure such as Amazon Web Service's (AWS) Glacier. Storage virtualization results in cost savings, logical abstraction and decoupling, data governance compliance, the bridging of structured and unstructured data, and increased productivity.

Implementing an enterprise data virtualization software solution that grants access to multiple, disparate data sources and produces datasets and IT-curated data services results in less complexity and overhead costs and faster access to data. The right virtual platform has advanced features such as sophisticated data management that ensure the integrity and consistency of data in a single source that's governed and secure. A virtualized application gives end-users immediate access to all data across business functions, providing actionable insights in real time.

The agility, flexibility, and compatibility of a virtual platform outshines traditional data warehousing and ETL. Virtually integrating data into a virtual environment results in less physical overhead costs and lower operational costs. A good choice of virtualization software is compatible with different operating systems, and features orchestrated data services, centralized metadata control, advanced query engines, web UI, and data governance and security.

Data Virtualization Use Cases

An Explanation Of Virtualization Software In Simple Terms

There are many uses for data and server virtualization. Virtualization inserts a virtual layer of data between disparate sources and data users. Implementing a virtual environment aids data integration by bridging the gaps between data sources. Server virtualization results in logical data warehouses with similar functions to traditional data warehouses. There's no need for a data infrastructure with a virtual server since existing data stores are used. A logical data warehouse federates all data sources into a single platform for integration with data services.

Having a single source of quality big data that's structured is key to predictive analytics. Big data can be generated from a variety of sources such as an Oracle database, smartphones, social media, and email. Data virtualization has plenty of operational uses, especially when dealing with siloed data. Virtualization provides abstraction and decoupling in addition to unification so that specific data sources can be isolated and accessed.

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