Data Virtualization is the new kid on the block for data integration. The technology has matured and been adopted by numerous organizations to make data available to a wide range of business users. Vendors such as Data Virtuality, Denodo, fraXses, IBM,RedHat, Stonebond, and Tibco have developed scalable, stable, and function-rich data virtualization platforms.
In a nutshell, data virtualization allows organizations to integrate data in a more flexible way than they are used to. This is important because data is increasingly becoming a crucial asset for organizations to survive in today’s fast-moving business world. In addition, data becomes more valuable if enriched and/or fused with other data. Unfortunately, enterprise data is dispersed by most organizations over numerous systems all using different technologies. To bring all that data together is and has always been a major technological challenge.In addition, more and more data is available outside the traditional enterprise systems. It's stored in big data platforms, in cloud applications, spreadsheets, simple file systems, in weblogs, in social media systems, in traditional databases, and so on.
This day focuses on Data Virtualization. The technology is explained; advantages and disadvantages are discussed; products are compared; use cases are discussed; and guidelines, tips, do’s, and don’ts are explained in detail.
You Will Learn
- How data virtualization could be used to integrate data in a more agile way
- How to embed data virtualization in business intelligence systems
- How data virtualization can be used for integrating on-premises and cloud applications
- How data virtualization products work
- How to avoid well-known pitfalls
- How to learn from real-life experiences with data virtualization
- What the guidelines, do’s, and don’ts are
Business intelligence specialists; data analysts; data warehouse designers; business analysts; data scientists; technology planners; technical architects; enterprise architects; IT consultants; IT strategists; systems analysts; database developers; database administrators; solutions architects; data architects; IT managers