About This Course
In today's data-driven economy, the quality of an organisation's database design directly determines the reliability, performance, and scalability of its information systems. Poorly structured databases lead to data redundancy, integrity failures, slow query performance, and costly redesigns — all of which undermine operational efficiency and strategic decision-making. Data Modeling and Database Design Training addresses this challenge by providing participants with a comprehensive, structured pathway from foundational data concepts to advanced physical database design.
This ten-day programme is built around real-world application. Participants begin by developing a deep understanding of data modelling theory — including conceptual, logical, and physical modelling — before progressing into entity-relationship diagramming, normalisation techniques, and the principles of relational database design. The curriculum then expands into more advanced areas such as dimensional modelling for data warehousing, NoSQL data models, indexing strategies, and performance tuning, ensuring participants are equipped for both transactional and analytical environments.
Delivered by experienced database architects and practitioners, the course combines structured instruction with hands-on design exercises, peer review of data models, and case studies drawn from real implementation scenarios across sectors including finance, healthcare, retail, and public administration. Participants leave with a complete design portfolio and the practical confidence to lead database design initiatives within their organisations.
TrainingComplex has designed this programme for professionals who work at the intersection of business analysis, systems development, and data management. Whether you are a systems analyst translating business requirements into data structures, a developer implementing database schemas, or a data architect overseeing enterprise information architecture, this course provides the depth and rigour needed to excel in your role.
Who Should Attend
- Database Administrators seeking to formalise and deepen their design skills
- Systems Analysts and Business Analysts involved in requirements gathering and system specification
- Software Developers and Application Architects who design and interact with relational databases
- Data Engineers building data pipelines and warehouse structures
- IT Project Managers overseeing system development and database implementation projects
- Enterprise Architects responsible for organisational data strategy and information architecture
- BI Developers and Data Analysts working with dimensional models and reporting databases
- ICT Officers in government and public sector institutions managing administrative information systems
Learning Objectives
- Explain the three-tier data modelling framework — conceptual, logical, and physical — and apply each tier appropriately in a design project
- Construct entity-relationship diagrams (ERDs) that accurately capture business entities, attributes, and relationships
- Apply first, second, third normal form and Boyce-Codd normal form to eliminate data redundancy and ensure referential integrity
- Design relational database schemas that enforce business rules through primary keys, foreign keys, and constraints
- Develop dimensional models — including star and snowflake schemas — suited for data warehousing and analytical reporting
- Write and optimise SQL Data Definition Language (DDL) scripts to implement physical database structures
- Apply indexing strategies and query optimisation techniques to improve database performance
- Design data models for NoSQL environments including document stores and wide-column databases
- Conduct structured data model reviews and communicate design decisions to technical and non-technical stakeholders
- Manage data model versioning, documentation, and governance throughout the database lifecycle
- Evaluate trade-offs between normalisation and denormalisation in operational versus analytical contexts
- Integrate data security, access control, and audit trail requirements into physical database design
- Develop a complete end-to-end data model and database schema for a realistic business scenario
Course Modules
Module 1: Foundations of Data and Information Architecture
- The role of data modelling in system development and enterprise architecture
- Types of data: structured, semi-structured, and unstructured — and their modelling implications
- Overview of database management systems: relational, columnar, document, and graph
- The three-schema architecture: external, conceptual, and internal views
- Data modelling standards and notations: an overview of IDEF1X, UML, and ER approaches and when to apply each
- The data lifecycle: how data is created, stored, used, archived, and retired, and why each stage has design consequences
Module 2: Conceptual Data Modelling
- Identifying business entities and their real-world meaning
- Defining entity attributes: simple, composite, derived, and multi-valued
- Capturing entity relationships: one-to-one, one-to-many, and many-to-many
- Constructing and reading entity-relationship diagrams using Chen and Crow's Foot notation
- Defining cardinality and optionality: distinguishing mandatory from optional participation in relationships
- Iterative refinement of conceptual models through stakeholder validation and business rule clarification
Module 3: Logical Data Modelling and the Relational Model
- Translating conceptual ERDs into relational tables and schemas
- Defining primary keys, candidate keys, surrogate keys, and foreign keys
- Handling weak entities, associative entities, and inheritance in relational design
- Documenting logical data models with data dictionaries and attribute specifications
- Mapping multi-valued attributes and derived attributes to relational structures with appropriate trade-off analysis
- Applying naming conventions and modelling standards consistently across schemas to support maintainability and team collaboration
Module 4: Normalisation and Data Integrity
- Understanding functional dependencies and their role in normalisation
- Applying first normal form (1NF), second normal form (2NF), and third normal form (3NF)
- Boyce-Codd normal form (BCNF) and when to apply fourth and fifth normal forms
- Enforcing data integrity through constraints: NOT NULL, UNIQUE, CHECK, and referential integrity
- Recognising and resolving transitive, partial, and multi-valued dependencies through worked decomposition examples
- Balancing normalisation against practical performance needs: understanding when controlled denormalisation is a justified design decision
Module 5: Physical Database Design
- Translating logical schemas into physical database structures for a target RDBMS
- Data type selection and storage optimisation for columns and tables
- Designing table partitioning, clustering, and tablespace allocation strategies
- Writing SQL DDL scripts: CREATE TABLE, ALTER TABLE, and constraint definitions
- Capacity planning and storage estimation: sizing tables and indexes based on row volume, growth rates, and retention policies
- Physical design differences across major RDBMS platforms: PostgreSQL, SQL Server, Oracle, and MySQL — dialect considerations and platform-specific features
Module 6: Indexing, Query Optimisation, and Performance Tuning
- Types of indexes: B-tree, hash, composite, covering, and partial indexes
- Analysing query execution plans to identify bottlenecks and full table scans
- Denormalisation strategies and their appropriate use in high-read environments
- Materialised views and caching approaches to improve analytical query performance
- Index maintenance overhead: understanding the write performance cost of indexes and strategies for managing index bloat
- Statistics and query planner behaviour: how the database engine uses table statistics to choose execution plans, and how to keep statistics current
Module 7: Advanced Relationship Modelling and Design Patterns
- Modelling complex relationships: ternary associations, recursive relationships, and hierarchies
- Supertype and subtype modelling (generalisation and specialisation) in database design
- Common design patterns: lookup tables, audit trails, soft deletes, and temporal data
- Handling many-to-many relationships with intersection tables and additional attributes
- Implementing versioned and bi-temporal data models to track both valid time and transaction time across changing records
- Designing flexible attribute models: entity-attribute-value (EAV) patterns, their appropriate use cases, and the risks of overuse
Module 8: Dimensional Modelling for Data Warehousing
- Principles of dimensional modelling: facts, dimensions, measures, and granularity
- Star schema design: fact tables, dimension tables, and conformed dimensions
- Snowflake schema and galaxy schema — when and why to use each approach
- Slowly changing dimensions (SCD Types 1, 2, and 3) and their implementation
- Designing factless fact tables and accumulating snapshot fact tables to capture events and process milestones
- Junk dimensions and degenerate dimensions: consolidating low-cardinality flags and retaining transactional keys without separate dimension tables
Module 9: NoSQL Data Modelling
- Overview of NoSQL paradigms: document, key-value, wide-column, and graph databases
- Data modelling for document stores: embedding versus referencing in MongoDB-style design
- Modelling wide-column data in Apache Cassandra: partition keys, clustering columns, and query-driven design
- Choosing between relational and NoSQL models based on access patterns, scale, and consistency requirements
- CAP theorem and consistency models: understanding eventual consistency, tunable consistency, and how they constrain NoSQL schema design decisions
- Schema evolution in NoSQL systems: strategies for managing structural changes in schema-less and schema-flexible stores without downtime or data loss
Module 10: Data Security, Governance, and Compliance in Database Design
- Embedding access control into database design: roles, privileges, and row-level security
- Designing for data privacy compliance: masking, pseudonymisation, and encryption at rest
- Audit trail design: capturing who changed what and when using temporal and history tables
- Data governance principles: ownership, lineage, classification, and metadata management
- Designing for regulatory frameworks: mapping GDPR, HIPAA, and similar requirements to concrete schema-level and storage-level controls
- Data retention and purging strategies: building expiry, archival, and right-to-erasure support into the database design from the outset
Module 11: Data Model Documentation, Versioning, and Communication
- Creating professional data model documentation: ERDs, data dictionaries, and design rationale
- Version control for database schemas using tools such as Liquibase and Flyway
- Communicating data model decisions to non-technical business stakeholders
- Conducting structured data model review sessions and incorporating feedback
- Maintaining a model change log: recording design decisions, rejected alternatives, and the business rationale behind each significant structural choice
- Tooling landscape for data modelling: comparing diagramming and repository tools such as erwin, Lucidchart, DbSchema, and dbt for documentation and collaboration
Module 12: Database Design for Emerging and Specialised Contexts
- Designing databases for cloud-native environments: managed services and serverless databases
- Multi-tenancy data models: shared schema, shared database, and isolated schema approaches
- Time-series data modelling for IoT, monitoring, and financial tick data
- Graph database design: nodes, edges, properties, and use cases in fraud detection and knowledge graphs
- Event-sourcing and CQRS patterns: designing append-only event stores and separate read models to support high-audit and high-throughput application architectures
- Polyglot persistence architectures: designing systems that combine multiple database technologies and defining clear boundaries for where each store owns its data
Module 13: Capstone Project: End-to-End Database Design
- Requirements analysis and entity discovery for an assigned business domain
- Developing a complete conceptual and logical data model with full ERD documentation
- Physical schema implementation using SQL DDL with indexing and constraint definitions
- Presentation and peer review of the completed database design with structured feedback
- Documenting design decisions and trade-offs: producing a written rationale that explains normalisation choices, key selection, and any intentional denormalisation
- Iterating the design in response to review feedback: revising the model to address identified issues and demonstrating understanding of the change's downstream impact
Requirements & Prerequisites
Participants should have a working knowledge of basic database concepts such as tables, rows, columns, and queries. Familiarity with SQL at a foundational level — including SELECT, INSERT, UPDATE, and DELETE statements — is recommended prior to attending. Prior exposure to any relational database platform such as MySQL, PostgreSQL, Oracle, or Microsoft SQL Server will be beneficial, though not mandatory.
Training Methodology
- Guided ERD workshops where participants progressively build entity-relationship diagrams from a set of business requirements narratives
- Normalisation drills using real-world data sets drawn from finance, HR, and public sector scenarios to practise applying normal forms step by step
- Schema design reviews conducted in small peer groups, simulating the kind of structured design walkthroughs used in professional database development teams
- Hands-on SQL DDL labs in which participants implement their logical models as physical schemas on a live relational database engine
- Dimensional modelling case studies based on actual data warehouse implementations, analysing design choices and their performance implications
- Comparative analysis exercises contrasting relational and NoSQL data models for different use cases, using tool-based exploration
- Performance tuning experiments where participants test the impact of indexing strategies and query rewrites on a pre-loaded database instance
- Capstone project in which each participant or team designs, documents, and presents a complete data model and physical schema for an assigned business domain
Organisational Benefits
- Improved database performance and reliability through adoption of sound design principles
- Reduction in costly database redesigns caused by poor initial modelling decisions
- Stronger alignment between business requirements and technical database implementations
- Enhanced data quality and integrity across organisational information systems
- Faster development cycles as developers work from well-structured, unambiguous data models
- Increased capacity of in-house teams to design and maintain complex databases without over-reliance on external consultants
- Better-informed ICT investment decisions through data architecture that scales with organisational growth
- Improved compliance with data governance, privacy, and audit requirements embedded in database design
Where Is Data Modeling and Database Design Training Offered?
This course runs in-person across: Johannesburg, Mombasa, Nakuru, Lagos, Riyadh, Kigali, Naivasha, Accra, Kampala, Dar es Salaam, Cape Town, Zanzibar, Addis Ababa, Nairobi, Dubai, Kisumu, Abuja, Pretoria, Arusha, plus Live Virtual (online) cohorts. Browse live dates for your city below.
Upcoming Schedules
| Location | Format | Dates | Price |
|---|
| Johannesburg, South Africa |
In-Person |
5 Oct 2026 – 16 Oct 2026 |
USD 7,000 |
| Mombasa, Kenya |
In-Person |
5 Oct 2026 – 16 Oct 2026 |
KES 230,000 |
| Nakuru, Kenya |
In-Person |
12 Oct 2026 – 23 Oct 2026 |
KES 230,000 |
| Lagos, Nigeria |
In-Person |
12 Oct 2026 – 23 Oct 2026 |
USD 5,000 |
| Riyadh, Saudi Arabia |
In-Person |
19 Oct 2026 – 30 Oct 2026 |
USD 9,000 |
| Kigali, Rwanda |
In-Person |
19 Oct 2026 – 28 Oct 2026 |
USD 3,800 |
| Naivasha, Kenya |
In-Person |
19 Oct 2026 – 28 Oct 2026 |
KES 240,000 |
| Accra, Ghana |
In-Person |
19 Oct 2026 – 28 Oct 2026 |
USD 7,600 |
| Kampala, Uganda |
In-Person |
19 Oct 2026 – 28 Oct 2026 |
USD 3,800 |
| Dar es Salaam, Tanzania |
In-Person |
19 Oct 2026 – 30 Oct 2026 |
USD 4,600 |
| Cape Town, South Africa |
In-Person |
19 Oct 2026 – 28 Oct 2026 |
USD 7,600 |
| Live Virtual |
Online |
24 Oct 2026 – 13 Dec 2026 |
KES 160,000 |
| Zanzibar, Tanzania |
In-Person |
26 Oct 2026 – 4 Nov 2026 |
USD 5,000 |
| Addis Ababa, Ethiopia |
In-Person |
26 Oct 2026 – 4 Nov 2026 |
USD 4,800 |
| Nairobi, Kenya |
In-Person |
26 Oct 2026 – 4 Nov 2026 |
KES 230,000 |
+237 more dates — view live schedule →
Frequently Asked Questions
Do I need to be an experienced SQL developer to attend this course?
A basic working knowledge of SQL is recommended — you should be comfortable with fundamental SELECT, INSERT, UPDATE, and DELETE statements. However, you do not need to be an advanced SQL developer, as the course focuses on design principles and the SQL covered is taught progressively within the programme.
What database platforms are used during the hands-on labs?
The practical labs primarily use PostgreSQL, which is freely available and widely adopted. Concepts covered are transferable to other major platforms including MySQL, Oracle Database, and Microsoft SQL Server, and facilitators will highlight platform-specific differences where relevant.
Will I receive a certificate upon completing the course?
Yes. Participants who attend the full programme and complete the capstone project will receive a Certificate of Completion from TrainingComplex, confirming their proficiency in data modelling and database design at an intermediate-to-advanced level.
Is this course suitable for someone transitioning from business analysis into a more technical role?
Absolutely. Business analysts who need to move into data modelling or systems design roles will find the course particularly valuable, as it begins with requirements-driven conceptual modelling and builds progressively toward technical implementation — bridging both worlds effectively.
Can the course be delivered on-site for our organisation's team?
Yes. TrainingComplex offers dedicated in-house delivery for organisational teams of five or more participants. The content can be contextualised to your specific industry sector, database platforms in use, and current design challenges, making it highly relevant for your team's immediate needs.
