• Tedamoh ACADEMY

    Data Modeling Masterclass

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Competence in data modeling

Why do we spend millions of euros and thousands of hours every year developing solutions that don't work?

Poor capture and formulation of business requirements leads to a huge waste of resources. Data models prevent this waste of time and money by capturing business terminology and requirements in precise form and at multiple levels of detail, ensuring fluid communication between business and IT.

Complete the Data Modeling Master Class to gain competency in data modeling. The Data Modeling Master Class is the training on data modeling and is offered several times a year to the public or as a special virtual training just for your team.

This training requires no prior knowledge of data modeling and is therefore not tied to any specific prerequisites.

 


 

Topics

 This training is being taught in German.

This Master Class is a complete data modeling 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.

This Data Modeling Master Class includes three parts with over 30 exercises and 20 hours of instruction:

Basics

After completing Part 1, you will be able to explain the benefits of data modeling, apply the five specifications for creating a data model, and properly use each of the data modeling components (entities, attributes, representatives, relationships, subtyping, keys, hierarchies, and networks).

Schema

By the end of Part 2, you will be proficient in creating conceptual, logical, and physical relational and dimensional data models. You will also be able to distinguish NoSQL data models from traditional models in terms of modeling structure and approach.

Scorecard

With the completion of Part 3, you will be able to apply best practices for data models using the ten categories of the Data Model Scorecard®. These categories are correctness, completeness, schema, structure, abstraction, standards, readability, definitions, consistency, and data.

After completing the Data Modeling Master Class, you will not only know how to create a data model, but also how to create a data model really well. Using the Data Model Scorecard®, you will be able to build supporting and complementary features into your data model and evaluate the quality of any data model.

Case studies and the exercises reinforce the material and allow you to apply what you have learned to your current projects.

Top 10 Learning Objectives

  • Explain data modeling components and identify them on your projects by following a question-driven approach
  • Demonstrate reading a data model of any size and complexity with the same confidence as reading a book
  • Validate any data model with key "settings" (scope, abstraction, timeframe, function, and format) as well as through the Data Model Scorecard®
  • Apply requirements elicitation techniques including interviewing, artifact analysis, prototyping, and job shadowing
  • Build relational and dimensional conceptual and logical data models, and know the tradeoffs on the physical side for both RDBMS and NoSQL solutions
  • Practice finding structural soundness issues and standards violations
  • Recognize when to use abstraction and where patterns and industry data models can give us a great head start
  • Use a series of templates for capturing and validating requirements, and for data profiling
  • Evaluate definitions for clarity, completeness, and correctness
  • Leverage the Data Vault and enterprise data model for a successful enterprise architecture

 

Prerequisites

This course assumes no prior data modeling knowledge and, therefore, there are no prerequisites. This course is designed for anyone with one or more of these terms in their job title: "data", "analyst", "architect", "developer", "database", and "modeler".