Data modelling
In this training you learn how to design effective data models that align with both operational and analytical needs. The session starts with the basic principles of data modelling and normalisation, and shows how relational models arose and are applied in modern data systems.
Next, the difference between OLTP and OLAP systems is covered and why these different approaches call for different data models. Attendees get acquainted with dimensional modelling and commonly used models such as the star schema and snowflake schema. More advanced models such as Data Vault and Anchor modelling are also covered.
Besides the modelling itself, attention is paid to the broader context: how do you choose the right model, what does a data warehouse data flow look like and what role do aspects such as data quality, governance, security and privacy play. The training combines theory with practical exercises in which attendees design a data model themselves.
- Explaining what data modelling is and why it is important
- Applying normalisation principles (up to the most common normal forms)
- Understanding the difference between OLTP and OLAP and the impact on data models
- Recognising and applying dimensional data models (star and snowflake schemas)
- Dealing with changes in data through techniques such as slowly changing dimensions
- Understanding the basic principles of Data Vault and Anchor modelling
- Choosing a suitable data model based on business requirements and data strategy
- Gaining insight into the complete data warehouse data flow
- Taking data quality, governance, security and privacy into account within data models