Course introduction
Centralized data warehouses are often designed using either the star model method (Kimball style) or normalized structures (Inmon style). Both methods have had to be reconciled with their original purpose. The Data Vault method developed by Dan Linstedt is specifically designed for the design of large centralized data warehouses (EDWs). The model has grown in popularity all over the world and e.g. In the Netherlands, most data warehousing is carried out using the Data Vault method. Data Warehouse and Big Data Guru Rick van der Lans says anyone planning a data warehouse needs to research the Data Vault method and then decide whether to implement it.

The Data Vault method is well supported for data warehousing, which requires integration of multiple data sources, flexibility of change, and easy scalability. The method has a clear and straightforward way to handle data history. In particular, the method has focused on data tracing and auditing, which has proved important e.g. as regulatory requirements become more stringent (so-called compliance requirements). Even if the method is not suitable as such, the course teaches a wide range of useful information about EDW strain modeling methods.

The course is well suited as an introduction to the Data Vault 2.0 certification course, which can be found in our calendar as held by Cindi Meyersohn.

The founder of our company, Ari Hovi, was the world’s first certified Data Vault modeler and Europe’s first Data Vault 2.0 Certified Practitioner. We also have a direct connection to Data Vault developer Dan Linstedt.

Data Warehouse – architectures and modeling
modeling methods suitable for different architectures
problems of star modeling and traditional ER data warehouse modeling
objectives for a good modeling approach

Data Vault method background
the history of the method
underlying ideas and architecture
infrastructure

Data Vault method building blocks
hubs, satellites and links
examples and instructions
stages of progress
exercises

Data processing and quality issues
where data is processed and managed
new ideas for quality management

Traceability and history
why traceability is important
traceability methods
methods of history

Evaluation of the Data Vault model
areas of application, problems and solutions
Products that support DW Automation and Data Vault
The role of Data Vault in broader modeling

Data Vault 2.0 features
hash key
Methodology, architecture and model
data virtualization
Hadoop articulation included

Daily start and end of the training event: training 9: 00-16.15.
Daily start and end of training event: training 9.00-16.15.