1-Day Seminar

Data Virtualization for Agile Business Intelligence Systems

Video You can view a recent IRM UK webinar with Rick on Data Virtualization for Agile BI Systems by clicking here.

Click here for an in-house quote request or for further information regarding in-house training.

The way decisions are made in organisations is changing. The biggest change is that they have to react faster. Studies are supporting this. For example, a study done in March 2011 showed that 43% of enterprises find that making timely decisions is becoming more difficult.  Managers increasingly find they have less time to make decisions after business events occur. The consequence is that it must be possible to change existing reports faster, and that reports must be developed more quickly. From this we can include that our BI systems have to be more flexible, more agile.

In addition, new forms of reporting and analytics are being requested by the user community, such as operational analytics, 360˚ reporting, exploratory analysis, deep and big data analytics, self-service BI, and semi-structured and unstructured data analytics.

All these new requirements demand that BI systems are developed in a more agile way. One of the technologies making this possible today is data virtualization. In a nutshell, data virtualization decouples data sources from the application and reports, and by doing that it can present a heterogeneous set of data stores as one logical database to all the reports. Compared to ETL, where data integration takes place in a scheduled manner, with data virtualization data is integrated on-demand.

The last few years, various data virtualization servers have become available to develop these systems, including those of Composite, Denodo, IBM, Informatica, Information Builders, Queplix and RedHat. In many projects they have already proven that data virtualization technology is mature, does simplify BI systems, and makes them more severely more agile.

This one day seminar focuses on data virtualization when deployed in business intelligence systems. The advantages of data virtualization are explained; products are compared, application areas are discussed; and the relationship with related topics, such as MDM, data governance, and SOA are also discussed.

Learning Objectives

  • How business intelligence systems could benefit from data virtualization
  • How to select the right business intelligence architecture
  • How to migrate to a more agile business intelligence system
  • How data virtualization products work
  • How to avoid well-known pitfalls
  • Learn from real-life experiences with data virtualization

Seminar Outline

 Introduction to data virtualization

  • What is data virtualization?
  • Differences between abstraction, data federation and data integration
  • Open versus closed data virtualization servers
  • Product overview, including those of Composite, Denodo, IBM, Informatica, Information Builders, Queplix and RedHat

The changing world of data warehousing and business intelligence

  • The new forms of business Intelligence
  • The role of the data warehouse
  • Do we still need staging areas, data marts, cubes and operational data stores
  • ETL for data transformation and data integration
  • What is a business intelligence architecture?
  • Disadvantages of classic business intelligence systems

Under the hood of a data virtualization server

  • Defining virtual tables, foreign tables and mappings
  • Exposing virtual tables
  • Stacking virtual tables
  • Importing non-relational Data, such as XML documents, web services, NoSQL databases – big data, dimensional cubes and unstructured data
  • Impact analysis and lineage
  • Running transactions – updating data

Caching for performance and scalability

  • 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
  • Cache replication for scalability

Query optimization techniques

  • Differences between the optimizer of a database server and the one of a data virtualization server
  • The ten stages of query processing
  • The optimizer of a data virtualization server
  • Different optimization techniques, including query substitution, pushdown, query expansion, ship joins, sort-merge Joins, statistical data and SQL override

A business intelligence architecture based on data virtualization

  • 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

Data Virtualization and SOA

  • Developing data services with a data virtualization server
  • Updating data through a data service

Data virtualization and master data management

  • What is 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

Data virtualization, information management and data governance

  • Impact of data virtualization on information management
  • Impact of data virtualization on data governance
  • Developing data services with a data virtualization server
  • On-demand data profiling and data cleansing
  • The need for upstream data cleansing

 The future of data virtualization

  • Data virtualization as driving force for data integration
  • More memory – More SSD
  • Potential new product features


  • Business Intelligence Specialists
  • Data Warehouse Designers
  • Business Analysts
  • Technology Planners
  • Technical Architects
  • Enterprise Architects
  • IT Consultants
  • IT Strategists
  • Information and Data Analysts
  • Database Developers
  • Database Administrators
  • Solutions Architects
  • Data Architects
  • IT Managers

Speaker Biography

Rick van der Lans

Rick F. van der Lans is an independent consultant, author and lecturer specialising in business intelligence, data warehousing and database technology. He is the Managing Director of R20/Consultancy. Rick has advised many large companies worldwide on defining their data warehouse architectures. He is the chairman of the European BI and Data Warehousing Conference (organised annually in London), and a columnist for two major newspapers in the Benelux, and he writes regularly for the B-eye-Network.

In-House Training
If you require a quote for running this course in-house, please contact us with the following details:

  • Subject matter and/or speaker required
  • Estimated number of delegates
  • Location (town, country)
  • Number of days required (if different from the public course)
  • Preferred date

Please contact:
Jeanette Hall
E-mail: jeanette.hall@irmuk.co.uk
Telephone: +44 (0)20 8866 8366
Fax: +44 (0) 2036 277202

Speaker: Rick van der Lans
Rick van der Lans

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Endorsed by:
DAMA International
  UK Chapter

IRM UK Conferences

Innovation, Business Change, and Technology Forum Europe 2017
21-22 March 2017, London

2 co-located conferences
Data Governance Conference Europe 2017
MDM Summit Europe 2017
15-18 May 2017, London

Business Analysis Conference Europe 2017
25-27 September 2017, London

2 co-located conferences
Enterprise Architecture Conference Europe 2017
BPM Conference Europe 2017
16-19 October 2017, London

2 co-located conferences
Business Intelligence & Analytics Conference Europe 2017
Enterprise Data Conference Europe 2017
20-23 November 2017, London

Click here to purchase past conference documentation.