In the foundation of any robust data architecture, mastering Data Modeling and Schema Design is a critical skill for ensuring that data is organized, stored, and managed efficiently, thereby enabling accurate analysis, seamless application development, and scalable data systems. A well-designed schema is the blueprint for a database, directly impacting performance, data integrity, and the ease with which business insights can be extracted. This comprehensive training course is meticulously designed to equip data architects, database administrators, data analysts, and software developers with the advanced knowledge and practical strategies required to understand, design, and implement effective data models for a variety of business needs, from transactional systems to analytical warehouses. Without robust expertise in Data Modeling and Schema Design, organizations risk building inefficient databases, facing data integrity issues, and struggling with poor performance that hinders their ability to make timely, data-driven decisions, underscoring the vital need for specialized expertise in this critical domain.

Duration: 10 Days

Target Audience

• Data Architects and Database Designers
• Database Administrators (DBAs)
• Data Analysts and BI Developers
• Software Developers and Engineers
• Data Engineers and ETL Developers
• Business Analysts and System Analysts
• IT Project Managers
• Data Governance and Quality Professionals
• Anyone responsible for designing or managing database systems.

Objectives

• Understand the fundamental concepts and purposes of data modeling.
• Learn about the different types of data models: conceptual, logical, and physical.
• Acquire skills in designing relational database schemas and applying normalization principles.
• Comprehend techniques for creating effective dimensional models for data warehousing.
• Explore strategies for schema design in NoSQL and non-relational databases.
• Understand the importance of data integrity, consistency, and constraints.
• Gain insights into the trade-offs between normalization and denormalization.
• Develop a practical understanding of Entity-Relationship (ER) modeling and its notation.
• Master the use of data modeling tools and software.
• Acquire skills in documenting and communicating data models effectively.
• Learn to apply data modeling best practices for performance optimization.
• Comprehend techniques for designing scalable and flexible database schemas.
• Explore strategies for modeling time-series data and handling slow-changing dimensions.
• Understand the importance of data governance and a unified data model.
• Develop the ability to lead and implement robust Data Modeling and Schema Design initiatives.

Course Content

Module 1: Introduction to Data Modeling Fundamentals

• What is data modeling and why is it essential?
• The role of a data model in a modern data architecture.
• The three-schema architecture: conceptual, logical, and physical models.
• Key concepts: entities, attributes, relationships, cardinality, and keys.
• The data modeling lifecycle: from requirements gathering to maintenance.

Module 2: Conceptual and Logical Data Modeling

• Purpose and techniques of conceptual data modeling.
• Using Entity-Relationship (ER) diagrams to visualize relationships.
• Introduction to standard ER notation (Crow's Foot, Chen, UML).
• Developing a logical data model: defining entities, attributes, and relationships without implementation details.
• Mapping business requirements to a logical model.

Module 3: Relational Database Design and Normalization

• Principles of relational theory and structured data.
• The importance of normalization for data integrity and redundancy reduction.
• The various normal forms: 1NF, 2NF, 3NF.
• Advanced normalization concepts: BCNF, 4NF, 5NF.
• The process of normalizing a database schema from a logical model.

Module 4: Physical Schema Design and Performance Optimization

• Translating a logical model into a physical schema.
• Choosing data types, indexes, and constraints for performance.
• Denormalization strategies for read-heavy applications and reporting.
• Partitioning, sharding, and other scalability techniques.
• Physical design considerations for specific database systems (e.g., MySQL, PostgreSQL, SQL Server).

Module 5: Dimensional Modeling for Data Warehousing

• Introduction to dimensional modeling and its purpose.
• Key concepts: facts, dimensions, measures, and granularity.
• Designing Star Schemas and Snowflake Schemas.
• Slowly Changing Dimensions (SCD) and their types (Type 1, 2, 3).
• Best practices for designing fact and dimension tables.

Module 6: Advanced Dimensional Modeling Techniques

• Bridge tables for many-to-many relationships.
• Factless fact tables and their uses.
• Handling different types of facts: transactional, periodic, and accumulating snapshots.
• Role-playing dimensions and conformed dimensions.
• Data modeling for OLAP cubes and other analytical tools.

Module 7: Data Modeling for NoSQL Databases

• Introduction to NoSQL paradigms: Key-Value, Document, Columnar, Graph.
• Document-oriented modeling: embedded documents vs. references.
• Columnar modeling for analytics and time-series data.
• Graph database modeling: nodes, relationships, and properties.
• The importance of query patterns in NoSQL schema design.

Module 8: Data Modeling Tools and Documentation

• Introduction to popular data modeling tools (e.g., Erwin, ER/Studio, DbSchema, draw.io).
• Using tools for forward engineering (generating DDL).
• Reverse engineering existing databases.
• Creating comprehensive documentation: data dictionaries, business glossaries, model diagrams.
• The role of documentation in communication and maintenance.

Module 9: Master Data Management (MDM) and Data Governance

• What is Master Data and why is it important?
• Strategies for identifying and modeling master data.
• The role of a central data model in a data governance program.
• Ensuring data quality and consistency across systems.
• Developing a common language and data standards.

Module 10: Data Vault Modeling

• Introduction to Data Vault modeling and its benefits.
• Core components: Hubs, Links, and Satellites.
• The purpose of Data Vault: providing a long-term historical view.
• Best practices for designing Data Vault schemas.
• Comparing Data Vault with Kimball (dimensional) and Inmon (relational) approaches.

Module 11: Modeling for Big Data and Data Lakes

• Schema-on-Read vs. Schema-on-Write for data lakes.
• Modeling semi-structured data (JSON, XML).
• Designing schemas for scalable file formats (e.g., Parquet, Avro).
• Data lake layers: raw, curated, and consumption.
• The role of a metadata repository (e.g., Hive Metastore).

Module 12: Business Requirements and Data Modeling

• The importance of gathering and analyzing business requirements.
• Using use cases and process diagrams to inform modeling.
• Translating business rules into data model constraints.
• Involving business stakeholders in the modeling process.
• Iterative and agile approaches to data modeling.

Module 13: Data Integrity and Constraints

• The role of keys (primary, foreign, unique) in maintaining integrity.
• Using constraints (NOT NULL, CHECK, DEFAULT) to enforce business rules.
• Referential integrity and cascade actions.
• Triggers and stored procedures for complex integrity rules.
• Auditing and logging data changes.

Module 14: Case Studies and Best Practices

• Analysis of successful and failed data models from various industries.
• Best practices for naming conventions and standardization.
• Common data modeling anti-patterns and how to avoid them.
• Performance tuning strategies related to schema design.
• The future of data modeling in a world of AI and machine learning.

Module 15: Practical Workshop: Designing a Schema from Scratch

• Participants work in teams on a business case with specific requirements.
• Exercise: Develop a conceptual model, logical model, and physical schema.
• Choose a modeling approach (relational, dimensional, NoSQL) and justify the choice.
• Design a data dictionary and documentation for the schema.
• Present the final schema design and receive peer feedback.

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.