Virtual Modelling for Data Virtualization

In the two-day Virtual Modeling for Data Virtualization training course, you will learn how to design and implement logical data models within a Data Virtualization architecture. You will discover how to build a flexible, modular, and semantic data layer—without physically replicating data—and how to bring together different data sources (cloud, on-premise, APIs, streaming, databases) into a single logical model.

The training combines Virtual Layering, Harmonization, Virtual Star Modeling, Virtual Subject Oriented Modeling, and Virtual Ensemble Logical Modeling (V-ELM) into a single coherent approach.

What can you expect?

During this training:

• Go through the different virtual layers: Introspection, Harmonization, Business, and Publication.
• Learn how to design a Virtual Data Layer that is both business-friendly and technically robust.
• Do you work with different model types such as:
  • Virtual Star Model
  • Virtual Subject Oriented Model
  • Harmonization Model
  • Virtual SuperNova (on Data Vault)
  • Virtual Ensemble Logical Model (V-ELM)
• Gain insight into performance, pushdown mechanisms, caching, and optimization.
• Address your security, metadata, lineage, and data catalog integration.
• Work with practical cases and lessons learned from real implementations.

What will you learn?

After completing the training, you will be able to:

• Designing a logical data model that is independent of physical storage.
• Apply different virtual modeling strategies depending on the use case (analytics, integration, data broker, application centric).
• Translating business concepts into a Virtual Ensemble Logical Model (V-ELM).
• Designing a Virtual Star Model for reporting and BI.
• Dealing with different data sources (SQL, NoSQL, APIs, XML, streaming).
• Determine performance and caching strategies (real-time vs. batch vs. cache).
• Integrate metadata, lineage, and security policies into your virtual model.
• Applying Privacy by Design (anonymization and pseudonymization in a virtual context).

Who is this training intended for?

This training is intended for professionals working on modern data architectures and data access:

• Data and information architects
• Data engineers
• BI specialists
• Data modelers
• IT architects
• Data governance professionals
• Anyone who wants to use Data Virtualization as a semantic data layer

The training is particularly valuable for organizations that work with hybrid architectures (cloud + on-premise) and want to accelerate data access without replication.

Training structure

The training combines:

• Conceptual explanation of virtual modeling principles
• Architecture considerations (storage, streaming, performance)
• Practical examples and modeling discussions
• Interactive group questions and scenarios
• Best practices en “what not to do”

Required prior knowledge

• Basiskennis van datamodellering (bijv. dimensioneel modelleren of Data Vault)
• Understanding of data warehousing or data integration
• Affinity with data architecture

Technical tool knowledge is not mandatory, but it helps to understand the concepts more quickly.

Why participate?

Data virtualisation is increasingly being used as a semantic layer above complex data landscapes. However, without proper virtual modelling, chaos, performance issues and unclear definitions arise.

Would you like to learn how to design a scalable, modular, and future-proof virtual data model that truly works in hybrid architectures?

Register today for Virtual Modelling for Data Virtualisation and become the architect of the logical data layer within your organisation.

Day 1:

• Introduction
• Virtual Layering: Introspection, Harmonization, Business & Presentation
• Model characteristics, pro’s and cons, scenario’s
• How doe virtual modelling work (non-technical)
• Modelling in a virtual world
  • Handling business/surrogate keys and relations
  • Types of data models
  • Metadata, reference data and masterd data.

`Day 2:

• Considerations for the data architecture
  • Location storage
  • Types and characteristics of data in DV (Stram / batch)
  • Characteristics of data sources for DV (Excel in memory, ability to push down workloads, impact on data strategy and performance)
• Best practices
  • Lessons learned
    – With DV
    – With data sources
  • What not to do with Data Virtualization
  • Data Virtualization as leverage for data management.

Our training courses are provided by experts with years of experience in data virtualisation and data integration:

• Jolanda van Gilst (Data Architect)
• Philip du Maine (Data Architect)

After completion, you will receive a Certificate of Professional Development as proof of the knowledge and skills you have acquired.