Data Virtualization: Technology and Use Cases
But why do we need a new technology? 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, and so on, and stored in traditional databases. For each system that requires data from several systems, different integration solutions are deployed. In other words, integration silos have been developed that over time has led to a complex integration labyrinth. The disadvantages are clear:
- Inconsistent integration specifications
- Inconsistent results
- Decreased time to market
- Increased development costs
- Increased maintenance costs
The bar for integration tools and technology has been raised: the integration labyrinth has to disappear. It must become easier to integrate data from multiple systems, and integration solutions should be easier to design and maintain to keep up with the fast changing business world.
All these new demands are changing the rules of the integration game, they demand that integration solutions are developed in a more agile way. One of the technologies making this possible today is Data Virtualization.
This seminar focuses on Data Virtualization. The technology is explained, advantages and disadvantages are discussed, products are compared, design guidelines are given, and use cases are discussed.
What 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-premised and Cloud applications
- How to migrate to a more agile integration system
- How Data Virtualization products work
- How to avoid well-known pitfalls
- How to learn from real-life experiences with Data Virtualization
- Introduction to Data Virtualization
- The changing world of data and application integration
- Under the hood of a Data Virtualization server
- Caching for performance and scalability
- Query optimization techniques
- Data Virtualization and the Logical Data Warehouse Architecture
- Data Virtualization and Big Data
- Data Virtualization and Master Data Management
- Data Virtualization, Information Management and Data Governance
- The future of Data Virtualization