Introduction

In the world of Business Intelligence and data warehousing, the effectiveness of analytical reporting and decision-making hinges critically on how data is organized and structured, making Data Modeling and Star Schema Design an indispensable skill for transforming raw, disparate information into a coherent, query-optimized, and user-friendly format. A well-designed data model, particularly the star schema, simplifies complex analytical queries, improves data retrieval performance, and empowers business users to explore data intuitively, unlocking profound insights that drive strategic growth. This training course is meticulously designed to equip data architects, BI developers, data engineers, database administrators, data analysts, and IT professionals with cutting-edge knowledge and practical skills in understanding foundational data modeling concepts, mastering the principles of dimensional modeling, designing efficient star and snowflake schemas, identifying facts and dimensions, handling various types of slowly changing dimensions, and leveraging best practices for building robust and scalable data models that serve as the backbone for effective business intelligence and analytical applications. Participants will gain a comprehensive understanding of how to conceptualize, design, and implement data models that ensure data integrity, enhance query performance, and empower organizations to derive maximum value from their data assets.

Duration

10 days

Target Audience

• Data Architects
• Business Intelligence (BI) Developers
• Data Engineers
• Data Modelers
• Database Administrators (DBAs)
• Data Analysts (seeking deeper technical understanding)
• ETL Developers
• Solution Architects
• Data Warehouse Professionals
• IT Professionals involved in data infrastructure

Objectives

• Understand the fundamental concepts of data modeling and its importance for BI.
• Master the principles of dimensional modeling (Kimball methodology).
• Learn to identify and design fact tables and various types of dimension tables.
• Develop proficiency in creating efficient Star Schemas and Snowflake Schemas.
• Understand how to handle Slowly Changing Dimensions (SCDs) effectively.
• Explore techniques for designing models for different business processes.
• Learn about aggregate tables and their role in query performance optimization.
• Develop skills in documenting data models and ensuring data governance.
• Understand the differences between transactional and analytical data models.
• Formulate strategies for building scalable and maintainable data warehouse models.
• Apply data modeling principles to solve real-world business intelligence challenges.

Course Content

Module 1. Introduction to Data Modeling for Business Intelligence

• Defining Data Modeling: Purpose, benefits, and types (conceptual, logical, physical)
• Importance of Data Modeling for BI: Performance, usability, consistency
• Comparison of OLTP (Online Transaction Processing) vs. OLAP (Online Analytical Processing) systems
• Evolution of data modeling: From relational to dimensional
• Overview of data warehousing concepts

Module 2. Foundational Data Modeling Concepts

• Entities and Attributes: Identifying key business objects and their properties
• Relationships: One-to-one, one-to-many, many-to-many
• Normalization vs. Denormalization: Trade-offs in data modeling
• Primary Keys and Foreign Keys: Ensuring data integrity
• Introduction to Entity-Relationship (ER) Diagrams

Module 3. Introduction to Dimensional Modeling (Kimball Methodology)

• Defining Dimensional Modeling: Its purpose for analytical queries
• Facts and Dimensions: The core components of a dimensional model
• The Business Process Approach: Identifying key business processes
• The Four-Step Dimensional Design Process
• Benefits of dimensional modeling for BI

Module 4. Designing Fact Tables

• Types of Fact Tables: Transactional, Periodic Snapshot, Accumulating Snapshot
• Fact Table Granularity: The level of detail in a fact table
• Measures: Additive, semi-additive, non-additive measures
• Surrogate Keys in Fact Tables
• Designing fact tables for specific business processes (e.g., sales, inventory)

Module 5. Designing Dimension Tables

• Characteristics of Dimension Tables: Descriptive attributes
• Date Dimension: Importance and common attributes
• Time Dimension and other common dimensions (e.g., Product, Customer, Geography)
• Junk Dimensions and Role-Playing Dimensions
• Degenerate Dimensions

Module 6. Star Schema Design

• Understanding the Star Schema: Central fact table, surrounding dimension tables
• Benefits of Star Schema: Simplicity, query performance, usability
• When to use a Star Schema
• Designing Star Schemas for various business scenarios
• Practical examples of Star Schema implementation

Module 7. Snowflake Schema Design

• Understanding the Snowflake Schema: Normalized dimensions
• Advantages and Disadvantages of Snowflake Schema: Storage, query complexity
• When to use a Snowflake Schema
• Converting a Star Schema to a Snowflake Schema
• Comparison of Star vs. Snowflake for different use cases

Module 8. Slowly Changing Dimensions (SCDs)

• Defining Slowly Changing Dimensions: Changes in dimension attributes over time
• SCD Type 1: Overwriting old values (no history)
• SCD Type 2: Creating new rows for changes (full history)
• SCD Type 3: Adding a new column for limited history
• Choosing the appropriate SCD type for business requirements

Module 9. Advanced Dimension Modeling Concepts

• Conformed Dimensions: Consistent dimensions across multiple fact tables/data marts
• Bridge Tables: Handling many-to-many relationships
• Outrigger Dimensions: Extending dimensions
• Hierarchies in Dimensions: Drill-down capabilities
• Recursive Hierarchies (e.g., organizational charts)

Module 10. Aggregate Tables and Performance Optimization

• What are Aggregate Tables?: Pre-calculated summary tables
• Benefits of Aggregates: Query performance, reduced data volume
• Designing Aggregate Tables: Granularity, measures
• Automatic Aggregates in BI Tools (e.g., Power BI, Tableau)
• When and how to implement aggregation strategies

Module 11. Data Modeling for Specific Business Processes

• Sales Data Model: Customer, Product, Time, Sales Fact
• Inventory Data Model: Product, Store, Time, Inventory Snapshot Fact
• Financial Data Model: Account, Department, Time, Financial Transaction Fact
• Human Resources Data Model
• Adapting dimensional modeling to unique business needs

Module 12. Data Modeling Tools and Techniques

• Conceptual Data Modeling: Whiteboarding, high-level diagrams
• Logical Data Modeling: ERD tools (e.g., ER/Studio, Lucidchart)
• Physical Data Modeling: Database-specific tools (e.g., SQL Server Management Studio)
• Data Modeling in BI Tools: Power BI Data Model View, Tableau Relationships
• Best practices for data model documentation

Module 13. Data Governance and Data Modeling

• Role of Data Governance in Data Modeling: Standards, policies, ownership
• Data Dictionary and Data Catalog: Documenting data model elements
• Ensuring data quality through model design
• Data Lineage: Tracking data flow through the model
• Collaboration between data modelers, business users, and IT

Module 14. Data Modeling for Data Lakes and Lakehouses

• Modeling in Data Lakes: Schema-on-read vs. Schema-on-write
• Data Lakehouse Concepts: Combining DW and Data Lake benefits
• Medallion Architecture (Bronze, Silver, Gold layers)
• Data Modeling for semi-structured and unstructured data
• Tools for data modeling in cloud data lakes (e.g., Databricks)

Module 15. Future Trends in Data Modeling

• Data Mesh and Data Fabric: Decentralized data architectures and their impact on modeling
• Graph Data Modeling: For complex relationships
• Automated Data Modeling: AI-driven suggestions
• Real-time Data Modeling for streaming analytics
• The evolving role of the data modeler in modern data ecosystems.

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: info@skillsforafrica.org, training@skillsforafrica.org Tel: +254 702 249 449

Training Venue

The training will be held at our Skills for Africa Training Institute Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Skills for Africa Training Institute certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: info@skillsforafrica.org, training@skillsforafrica.org Tel: +254 702 249 449

Terms of Payment: Unless otherwise agreed between the two parties’ payment of the course fee should be done 7 working days before commencement of the training.