Data virtualization is not a new technology anymore. Products from several vendors have been on the market for quite some time now. Over the years numerous organizations have adopted this technology to develop logical data warehouses, to develop 360-degree views of customer data, to democratize data, and to make access to any type of data for every data consumer more agile.
Where do you start with data virtualization? What are the design guidelines? How do you exploit this technology efficiently and effectively? These are just some of the many questions that organizations face when they start their data virtualization projects.
This session is aimed at helping organizations make their first strides with data virtualization. It starts with explaining the key concepts of data virtualization servers, including virtual tables, query optimization, query pushdown, and caching. The core of this session is devoted to explaining all the guidelines, tips, do’s, and don’ts when designing and implementingdata virtualization systems. In addition, use cases, architecture, design, and implementation are all discussed. The session is based on experiences coming from many data virtualization projects.
After this session attendees will have enough footholds to start their own data virtualization projects.
You Will Learn
- The building blocks of data virtualization: virtual tables
- How to layer virtual tables to create a flexible architecture/system; dividing functionality over the layers
- Which design technique should be deployed and when:normalization, denormalization, or star schemas?
- The relationship between poor virtual table design and bad query performance
- How to design to enforce query pushdown
- Rules for efficiently using caching of virtual tables
- How to handle defective data and master data
- How to determine whether a physical data warehouse is still a necessity
- Where data security is best implemented
- How to deal with different types of data consumers, from simple reporting via mobile apps to data scientists
- 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