With our TEDAMOH Academy we offer various seminars, trainings and workshops as well as certifications to support and expand continuous learning in everyday work.
You can either expand your theoretical knowledge by attending a seminar or training or acquire knowledge yourself in practice-oriented workshops. To learn a theory or methodology, such as how to handle temporal data, or to obtain certification in data modeling, it is best to visit our training section.
This Master Class is a complete data modelling course, containing five modules of practical techniques for producing conceptual, logical, and physical relational and dimensional and NoSQL data models.
After learning the styles and steps in capturing and modeling requirements, you will apply a best practices approach to building and validating data models through the Data Model Scorecard®. You will know not just how to build a data model, but how to build a data model well.
In this webinar, Dirk provides general information about the Data Modeling Certification (DMC) and the ten categories that the certification exam covers. Learn about the requirements for and benefits of the DMC, and learn more about preparing and registering for the exam. He presents the added value of the free performance assessment answers all your questions about the certification during the webinar.
With this package you book all webinars as well as the exam to obtain the Data Modeling Certification (DMC) in the period March to June 2023 for the package price of 499€ (excl. VAT).
The following webinars and the exam are included:
- DMC (01) - Categoy Syntax
- DMC (02) - Category Components
- DMC (03) - Category Process and Approach
- DMC (04) - Category Conceptual, Logical, and Physical
- DMC (05) - Category Relational and Dimensional
- DMC (06) - Category Notation
- DMC (07) - Category Abstraction
- DMC (08) - Category Standards
- DMC (09) - Category Definitions
- DMC (10) - Category Best practices and pitfalls
- DMC - Exam
This training will focus on methods and techniques for handling bitemporal data in a Data Warehouse. It includes how to populate and afterwards get bitemporal data out of the Data Warehouse’s core layer.
Nowadays, most data warehouses already store “some kind of history” of data. But what about events that took place at a different time than what the data warehouse represents to us? Or data that will be valid in the future? For example, future planned prices for products and goods or special prices for discount battles or sales promotions like the “Black Friday” in the United States.