Learn how to design and implement logical data models within a Data Virtualization architecture. Modelling techniques specific to data virtualisation: logical views, semantic layers and virtual data marts.
In the two-day Virtual Modelling for Data Virtualization training, you learn how to design and implement logical data models within a Data Virtualization architecture. You discover how to build a flexible, modular and semantic data layer, without replicating data physically, and how to bring different data sources (cloud, on-premise, APIs, streaming, databases) together in one logical model.
The training combines Virtual Layering, Harmonization, Virtual Star Modelling, Virtual Subject Oriented Modelling and Virtual Ensemble Logical Modeling (V-ELM) into one coherent approach.
What can you expect?
During this training:
You go through the different virtual layers: Introspection, Harmonization, Business and Publication.
You learn how to design a Virtual Data Layer that is business-friendly and technically robust.
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)
You gain insight into performance, pushdown mechanisms, caching and optimisation.
You cover security, metadata, lineage and data catalog integration.
You work with practical cases and lessons learned from real implementations.
What will you learn?
After this training you can:
Design a logical data model that is independent of physical storage.
Apply different virtual modelling strategies depending on the use case (analytics, integration, data broker, application centric).
Translate business concepts into a Virtual Ensemble Logical Model (V-ELM).
Design a Virtual Star Model for reporting and BI.
Deal 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.
Apply Privacy by Design (anonymisation and pseudonymisation in a virtual context).
Training setup
The training combines:
Conceptual explanation of virtual modelling principles
Architecture considerations (storage, streaming, performance)
Practical examples and modelling discussions
Interactive group questions and scenarios
Best practices and "what not to do"
Desired prior knowledge:
Basic knowledge of data modelling (e.g. dimensional modelling or Data Vault)
Understanding of data warehousing or data integration
Affinity with data architecture
Technical tool knowledge is not required, but helps to understand the concepts faster.
Why take part?
Data Virtualization is increasingly being used as a semantic layer on top of complex data landscapes. But without good virtual modelling, chaos, performance problems and unclear definitions emerge.
Want to learn how to design a scalable, modular and future-proof virtual data model that really works in hybrid architectures?
Sign up today for Virtual Modelling for Data Virtualization and become the architect of the logical data layer within your organisation.
Day 1:
Introduction
Virtual Layering: Introspection, Harmonization, Business and Presentation
Model characteristics, pros and cons, scenarios
How does virtual modelling work (non-technical)
Modelling in a virtual world
Handling business/surrogate keys and relations
Types of data models
Metadata, reference data and master data
Day 2:
Considerations for the data architecture
Location of storage
Types and characteristics of data in DV (Stream / 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.
After completing this training, you receive a Professional Development certificate as proof of the knowledge and skills you have gained.
This training is intended for professionals working on modern data architectures and data access:
Data and information architects
Data engineers
BI specialists
Data modellers
IT architects
Data governance professionals
Anyone who wants to use Data Virtualization as a semantic data layer
The training is particularly valuable for organisations working with hybrid architectures (cloud + on-premise) that want to accelerate data access without replication.
Basic knowledge of data modelling (e.g. dimensional modelling or Data Vault)
Understanding of data warehousing or data integration
Affinity with data architecture
Technical tool knowledge is not required, but helps to understand the concepts faster.
