Introduction to Data Virtualization: Technology and Use Cases
  • (0)9 241.56.13

    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

    This event is history, please check out the List of Upcoming Seminars

    These related seminars and workshops may also be of interest to you:

    Full Programme:
    13.30h - 14.00h
    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, 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
    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
    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 seminar

    And find out who presents this training ...

    Questions about this ? Interested but you can't attend ? Send us an email !


    I.T. Works has been organizing seminars and workshops on technical and business IT topics since 1992. Our high-quality, information-packed, vendor-independent events provide solutions to the problems that many IT and business professionals face today.


    I.T. Works
    Technologiepark 82, 9052 Gent
    Phone: +32 (9) 241.56.13
    Fax: +32 (9) 241.56.56
    BTW/VAT/RRRP: BE 0454.842.797

    © I.T. Works - All Rights Reserved