Introduction to Data Virtualization: Technology and Use Cases
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    Introduction to Data Virtualization: Technology and Use Cases

    Rick van der Lans explains the technology, compares products, and discusses advantages, disadvantages, and last but not least, some major use cases

      26 February 2019 (14-21h)
      Parker Hotel (Diegem)
      Price: 720 EUR (excl. 21% VAT)

      Presented in English by Rick van der Lans


    Full Programme:
    13.30h - 14.00h
    Registration and welcome of the participants with coffee/tea and croissants, and opportunity to network
    14.00h
    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, RedHat JBoss Data Virtualization, 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
    15.40h
    Coffee/tea, refreshments and opportunity to network
    16.00h
    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
     
    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
    17.45u
    Diner
    18.45u
    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
    20.45h
    Questions, summary and conclusions
    21.00h
    End of this seminar

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