The Dos and Don’ts of Data Virtualization Software


Organizations of any size generate a vast amount of big data daily without knowing how to leverage its value. Accessing and analyzing data remains a challenge for many businesses, which is where business intelligence tools come in. Data virtualization is a type of data integration technology that gives access to real-time data in a single source. This data integration technology creates integrated views of data across different sources without the need to migrate data.

What’s Data Virtualization?

Data virtualization software bridges multiple, disparate sources of data into a single source of data to fuel analytics. It creates a modern data layer giving users the ability to access, combine, transform, and deliver datasets efficiently and cost-effectively. Data virtualization technology grants fast data access to disparate data sources throughout an organization including databases, big data sources, and cloud and IoT systems. Business users can apply a range of analytics, such as visualized, streaming, and predictive analytics in real time. Integrated data governance and data security ensure business data is consistent, high-quality, and secure. Common data sources virtualized through data virtualization software include excel and flat files, data warehouses, data lakes, big data, cloud data, web services, and IoT data.

There are four main components that a strong data virtualization platform should have. Agile design and development are necessary to gather data, discover patterns, model and validate virtual views, and make the necessary modifications. High-performance runtime is needed to make a query that can be optimized to execute a single statement delivered in the right format. This results in real-time data that are optimized with a reduced risk of data errors. A data virtualization platform will need to cache essential business data when necessary, optimize queries, and deliver data in the proper format. The right solution also has a business directory that makes it easy to find specific business data.

The Basics of Data Virtualization


Data virtualization tools add value by creating a high-speed, virtualized data layer that gives self-service data access in an organized, cost-effective manner to applications analytics systems. Most data integration solutions start small with a focused team responsible for one or more projects. Project datasets are delivered as data layers are built. This addresses data challenges such as disparate sources, structured and unstructured data, real-time data, and data outside of the data warehouse. Data virtualization projects should be based on business value and ease of implementation. Data virtualization should reuse various data services in the application, business, and source layers.

Implementing the right data virtualization software grants access to multiple and varied data sources and delivers datasets and an IT-curated data services foundation for any solution. Data virtualization software provides sophisticated data management that removes bottlenecks and creates consistency in data. This is done by giving real-time access to data collected from disparate sources in a single platform that complies with data governance and security. Having real-time access to business data provides actionable insights that improve business intelligence and aid business decisions.

The Benefits of Data Virtualization


Organizations of any size can benefit from creating a single point of access for business data. The virtual layers created by data virtualization accelerates data analysis, which results in more in-depth insights that aid business intelligence with less effort than ETL. Reusable data services, interactive development, and validation reduce the risk of data errors by improving data quality and avoiding rework. Business users also enjoy a reduction in data management infrastructure costs.

There are several use cases for data virtualization. Within analytics, data virtualization can be applied to physical data integration prototyping, logical data warehouse, data preparation, and data governance constraints for data movement. Operational use cases include abstract data access layers, registry-style master data management, and application data access. Other emerging use cases for data virtualization include cloud data sharing, edge data access in IoT integration, data hub enablement, and data and content integration.


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