The Erwin Data Modeler course equips students with essential skills to design and maintain data models effectively, enhancing their understanding of data management concepts and enabling practical application within various organizational contexts. Throughout the course, you will learn key concepts such as entity-relationship modeling, normalization techniques, and data governance principles. The practical application of these topics empowers you to create and maintain comprehensive data models, ensuring data integrity and optimizing performance. By the end of this course, you will have the confidence to leverage Erwin Data Modeler in real-world scenarios, enhancing your ability to collaborate with teams and make informed decisions about data management.

Course Objectives:

In this course, you will learn to:

• Understand the fundamentals of data modeling and its importance in database design.
• Learn to create and maintain conceptual, logical, and physical data models using Erwin Data Modeler.
• Gain proficiency in applying normalization techniques to optimize data structures.
• Explore best practices for documenting and presenting data models effectively.
• Develop skills in model version control and collaboration within a team environment.
• Master the import and export functionality for data model integration with other tools.
• Understand the implications of data governance and its role in data modeling.
• Utilize advanced features like data lineage, impact analysis, and model comparisons.
• Learn to generate reports and visualizations from data models.
• Prepare for real-world application of skills in database administration and data architecture.

Course content

Introduction to Data Modeling

• What is Data Modeling?
• Importance of data modeling in database design
• Types of data models (Conceptual, Logical, and Physical)
• Understanding Erwin Data Modeler
• Overview of Erwin Data Modeler
• Features and benefits
• Installation and setup

Interface Overview

• Navigating the Erwin Data Modeler workspace
• Menus, toolbars, and panels
• Overview of key components (Diagrams, Model Explorer, and Properties Pane)
• Customizing the workspace
• Keyboard shortcuts and productivity tips

Creating a Conceptual Data Model

• Identifying entities and attributes
• Drawing relationships
• Cardinality and participation
• Primary and foreign keys
• Naming conventions and standards
• Generating a conceptual model

Building a Logical Data Model

• Transitioning from conceptual to logical models
• Adding data types and domains
• Refining keys and relationships
• Using naming standards and abbreviations
• Validation rules for logical models

Physical Data Modeling

• Converting logical models to physical models
• Database-specific features in physical modeling
• Indexes, partitions, and constraints
• Data types mapping for different databases
• Generating DDL (Data Definition Language) scripts

Advanced Modeling Techniques

• Using sub-models
• Managing large models with subject areas
• Reverse engineering
• Importing databases into Erwin Data Modeler
• Forward engineering
• Synchronizing databases with the model
• Working with views, triggers, and stored procedures

Team Collaboration Features

• Model versioning and governance
• Sharing and managing models using Mart Server
• Using naming and glossary standards
• Managing permissions and roles in team environments

Reporting and Documentation

• Generating reports
• Standard reports and custom reporting
• Model diagrams and metadata export
• Integrating Erwin with external tools (Excel, BI tools, etc.)

Automation and Scripting

• Using macros and scripts to automate tasks
• Customizing Erwin Data Modeler with API
• Common use cases for automation

Using Erwin’s advanced features

• Working With Design Layers
• What Is A Design Layer?
• Other Design Layer Hierarchies
• Creating New Design Layers
• Creating New Design L
• Splitting A Model
• Deriving A Model
• Adding a Model Source
• What Is A Transform?
• What Is A Transform?
• Transform Toolbar
• Applying Transforms
• Expected Transform Results
• Transforms And The Model Explorer
• Resolving And Reversing Transforms
• Synchronizing Changes Between Design Layers
• Preserving Model History
• How to design Dimensional Model using Erwin Training
• ETL Rules using CA Erwin Data Model

Best Practices and Real-World Examples

• Tips for effective data modeling
• Common challenges and how to overcome them
• Case studies and practical applications
• g., retail, healthcare, finance databases

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