Data virtualisation can simplify data access and improve agility. Three uses of virtual data sources are: 1) Merger and Acquisition: Due to merger of data warehouses, OLTP systems are changed which causes the ETL jobs to break. This situation can be avoided by introducing virtual data sources between source OLTP systems and the data warehouse or between data warehouses and data marts, 2) Stabilizing data warehouse ETL processing, 3) Simplifying self-service data preparation for business users to access data across multiple data sources. In conclusion, virtual data sources are useful in practice because of its ability to de-couple physical data sources from ETL processing improve design and introduce governance. Read more at: http://www.datavirtualizationblog.com/overlooked-capability-data-virtualisation/

To prepare for the General Data Protection Regulation, companies need to find a way to establish security controls over the entire infrastructure from a single point. Without investing in new hardware or re-building existing systems, data virtualization allows companies to quickly and easily comply with data protection regulations. There are three ways in which this can be done. First rule is not to replicate the data which will lead to governance and security nightmares. Second is that data virtualization makes it possible to apply consistent levels of security across the heterogeneous data sources which contains the data. Finally, data virtualization removes the need for replication and latency of updates of customer information, thus providing users accurate information as applied in the system of record. Thus, data virtualization can help in data protection. Read more at: http://www.datavirtualizationblog.com/3-steps-data-protection-compliance-gdpr/

Data federation and data virtualization are both terms that have been coined to describe the process of collecting and compiling data. Data virtualization is a superset of the ten-year old data federation technology. It also includes the advanced capabilities of performance optimization as well as self-service search and discovery. Data federation is a distributed access to data residing in multiple systems with the purpose of joining the data together as if it came from the same system. Data virtualization on the other hand, has evolved from data federation by improving performance and adding other advanced capabilities such as self-service search and discovery. Advanced data virtualization products like the Denodo Platform have evolved to include dynamic query optimization techniques to determine the best query execution plan thus delivering optimal performance times.  Read more at: http://www.datavirtualizationblog.com/leading-analysts-mixing-data-federation-data-virtualization-terms/