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Data Modelling Certification Training

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Reviews 4.9 (4.6k+)
Rated 4.7 out of 5

Learners : 1080

Duration :  25 Days

About Course

Our Data Modelling Online Training is designed to help learners master the concepts, tools, and techniques used to design efficient and scalable database structures. This course covers both conceptual, logical, and physical data modelling approaches, preparing you for real-world data management and analytics projects.

Through this training, you’ll learn how to analyze business requirements, translate them into data models, and implement them using industry-leading tools like ERwin, PowerDesigner, and SQL Developer Data Modeler. You’ll also gain practical experience in normalization, relationships, keys, constraints, and schema design across different database platforms such as Oracle, SQL Server, and Snowflake.

What You Will Learn

  • Fundamentals of data modelling and database design
  • Difference between conceptual, logical, and physical data models
  • Identifying entities, attributes, and relationships
  • Data normalization and denormalization
  • Dimensional modelling for data warehousing
  • Star and snowflake schema design
  • Best practices for performance and scalability
  • Working with real-world projects using ER diagrams

Why Enroll in This Course

  • Instructor-led online sessions with real-time examples
  • Hands-on practice with leading data modelling tools
  • Industry-based case studies and assignments
  • Assistance with Data Modelling Certification preparation
  • Job-oriented training for Data Analysts, BI Developers, and Data Engineers

Who Can Learn

This course is ideal for:

  • Database Developers and Administrators
  • Data Analysts and BI Professionals
  • Data Engineers and Architects
    Anyone interested in learning data structure and database design

Data Modelling Training Course Syllabus

Module 1 – Need for Data Modeling
  • What is data modeling and its significance?
  • What is Data Warehousing
Exploring the 12c Oracle Database Architecture
  • Conceptual Data modeling
  • Physical Data modeling
  • Logical Data modeling
  • Benefits and scope of data modeling
Module 3 – Multidimensional Data Modelling
  • Basic Conceptual Architecture
  • Demonstrate fact and dimension
  • Difference between facts and dimensions
Module 4 – Introduction to RDBMS
  • Evolution of RDBMS
  • RDMS providers
  • Parts of database
  • Relational concepts
Module 5 – RDBMS Vs Data warehouse and ETL Process
  • OLTP vs. OLAP
  • What is ETL Process
  • Where ETL Process is used
  • Scope of ETL in Data warehouse
Module 6 – Erwin-Design Layer Architecture
  • Topics for Erwin Tool
  • Introduction to Erwin Tool
  • Installation and configuration of erwin and Notation
  • Creation of Entities and attributes
  • Creation of Relationship
  • Understanding constraints (PK, FK) in Erwin tool
  • Creation of Domains, Default Values, Validation rules
  • Creating Diagrams
  • Creating Conceptual model
  • Creating Logical model
  • Creating Physical model
  • Creating Subject area model
  • Generating Scripts from Physical Model
  • Building reports and Printing in erwin
  • Forward Engineering
  • Reverse Engineering
  • Complete compare of models/schem
Data Modelling Course Key Features

Course completion certificate

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Data Modelling Online Training FAQ'S

What is Data Modelling, and why is it important?

Data Modelling is the process of creating a visual representation of an information system’s data structure and relationships. It helps define how data is stored, connected, and accessed.
Importance:

  • Ensures data consistency and accuracy
  • Simplifies database design and maintenance
  • Improves communication between business and technical teams
  • Supports scalability and performance optimization
What are the different types of Data Models?

There are three main types of data models:

  • Conceptual Data Model: High-level design focusing on entities and relationships.
  • Logical Data Model: Defines attributes, keys, and relationships in detail without database-specific implementation.
  • Physical Data Model: Represents how the data will be stored in the database, including tables, columns, indexes, and constraints.
What is Normalization, and why is it used?

Normalization is the process of organizing data in a database to reduce redundancy and improve data integrity. It divides large tables into smaller, related ones.
Common Normal Forms: 1NF, 2NF, 3NF, and BCNF.
Normalization ensures efficient data storage and simplifies updates or deletions without anomalies.

What is the difference between OLTP and OLAP Data Models?

OLTP (Online Transaction Processing): Used for daily operations; focuses on fast inserts, updates, and queries. It uses normalized data models.
OLAP (Online Analytical Processing): Used for analytics and reporting; focuses on query performance. It uses denormalized or dimensional data models (Star/Snowflake schema).

What are Star Schema and Snowflake Schema in Data Warehousing?
  • Star Schema: A central fact table connected to multiple dimension tables. Simple and easy to query.
  • Snowflake Schema: A more complex version of the star schema where dimension tables are normalized into multiple related tables. It saves storage but increases query complexity.

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