Get ready to expand your data integration capabilities, deliver business-speed information, and make the most of recent advances in data virtualization technology. Through a combination of lecture and case study review, you will learn how data virtualization works and how to position it in your data integration architecture and processes.
Conference Replay | Digital Course Book Included
The data integration landscape has changed radically the past few years. What was once a relatively manageable problem of blending and unifying data from enterprise transaction systems has grown to encompass external data, web data, clickstream data, end-user data, big data, cloud data, and more. New expectations for information-driven business agility further compound the complexities of modern data integration. The ETL-based data warehouse is no longer enough. Data virtualization is a core component of next-generation data integration architectures, techniques, and technology.
Get ready to expand your data integration capabilities, deliver business-speed information, and make the most of recent advances in data integration technology. Through a combination of lecture and case study review you will learn how data virtualization works and how to position it in your data integration architecture and processes.
What You Will Learn
- Data virtualization definitions and terminology
- Business case and technical rationale for data virtualization
- Key concepts and foundational principles of virtualization--views, services, etc.
- Data virtualization lifecycle, capabilities, and processes
- How to extend the data warehouse with virtualization
- How virtualization enables federation and enterprise data integration
- How virtualization is applied for big data and cloud data challenges
- How companies use virtualization to solve business problems and drive business agility
Continuing Professional Education Credits: 4
Apply these credits toward your CBIP recertification. Not certified? Learn more about CBIP and how to get certified here.
Blue Buffalo Group
Leader and key research contributor for group responsible for analyzing trends via market landscape analysis, technical research, and platform testing. Nearly 20 years of experience in areas related to business analytics and business intelligence in professional services, sales consulting, product management, industry analysis and research. Helped organizations to solve their analytics problems whether they related to operational platforms such as customer care or billing, or applied analytical applications, such as revenue assurance or fraud management. Established thought leadership in emerging data management paradigms such as big data (combination of multi-structured and relational data sets) applications and NoSQL access data stores.