Introduction to Data Virtualization: Technology and Use Cases

Introduction to Data Virtualization: Technology and Use Cases

12 June 2019 (9.30-17.30)
Location: Parker Hotel (Diegem)
Presented in English by Rick van der Lans
Price: 720 EUR (excl. 21% VAT)
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Full Programme:
8.30h - 9.30h
Registration and welcome of the participants with coffee/tea and croissants, and opportunity to network
Introduction to Data Virtualization
  • What is data virtualization ?
  • Use case of data virtualization: business intelligence, data science, democratizing of data, master data management, distributed data
  • Differences between data abstraction, data federation, and data integration
  • Open versus closed data virtualization servers
  • Market overview: AtScale, Cirro Data Hub, Data Virtuality, Denodo Platform, FraXses, IBM Data Virtualization Manager for z/OS, Stone Bond Enterprise Enabler, and TIBCO Data Virtualization
How Do Data Virtualization Servers Work ?
  • The key building block: the virtual table
  • Integrating data sources via virtual tables
  • Implementing transformation rules in virtual tables
  • Stacking virtual tables
  • Impact analysis and lineage
  • Running transactions – updating data
  • Securing access to data in virtual tables
  • Importing non-relational data, such as XML and JSON documents, web services, NoSQL, and Hadoop data
  • The importance of an integrated business glossary and centralization of metadata specifications
Coffee/tea, refreshments and opportunity to network
Performance Improving Features
  • Caching of a virtual table for improving query performance, creating consistent report results, or minimizing interference on source systems
  • Differences between full refreshing, incremental refreshing, live refreshing, online refreshing and offline refreshing
  • Different query optimization techniques, including query substitution, pushdown, query expansion, ship joins, sort-merge Joins, statistical data and SQL override
Use Case 1: The Logical Data Warehouse Architecture
  • The limitations of the classic data warehouse architecture
  • On-demand versus scheduled integration and transformation
  • Making a BI system more agile with data virtualization
  • The advantages of virtual data marts
  • Strategies for adopting data virtualization
  • Application areas of data virtualization
  • The need for powerful analytical database servers
  • Migrating to a data virtualization-based BI system
Use Case 2: Data Virtualization and Master Data Management
  • How can data virtualization help with creating a 360° view of business objects
  • Developing MDM with a data virtualization server – from a stored to a virtual solution
  • On-demand data profiling and data cleansing
Lunch Buffet together with the other group @itworks
Use Case 3: From the Physical Data Lake to the Logical Data Lake
  • Practical limitations of developing one physical data lake
  • Shortening the data preparation phase of data science with data virtualization
  • Sharing metadata specifications between data scientists
  • Implementing analytical models inside a data virtualization server
Use Case 4: Democratizing Enterprise Data
  • Increasing the business value of the data asset by making all the data available to a larger group of users within the organisation
  • The business value of consistent data integration
  • Using lean data integration to make data available for analytics and reporting faster
  • One consistent data view for the entire organisation
  • How the business glossary and search features help business users
  • The coming of the data marketplace
Use Case 5: Dealing with Big Data
  • Big data can be too big to move - data can't be transported to the place of integration
  • Data virtualization pushes data processing to where the data is produced
  • Hiding the physical location of the data
  • With data virtualization, the network becomes the database
Closing Remarks
  • The Future of Data Virtualization
  • Data virtualization as driving force for data integration
  • Potential new product features
Questions, summary and conclusions
End of this one-day seminar

This course is a pre-conference workshop of the BI & Data Analytics Summit, a large conference in Belgium with 2 keynote speakers, 12 case-based presentations, and over 100 participants.


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