Data Modelling in the Age of AI
Just like a city is built from careful blueprints, a data ecosystem is built from robust data models. Explore how modern AI tools can accelerate your workflow without replacing the need for thoughtful design.
Data Modelling Theory
Master the foundational theories of data modeling. Learn how to construct robust, logical schemas that translate business realities into structured data systems without writing a single line of SQL.
Schema Evolution & Versioning
Reading a schema's history like strata at a dig site â excavating each version carefully instead of bulldozing what came before.
Dimensional Modelling (Star & Snowflake)
Shaping data for the questions the business actually asks.
Entity-Relationship Design Patterns
Recurring blueprints for recurring business shapes.
Graph Data Modelling
Modelling relationships as first-class citizens, not foreign keys.
Master Data Management
One version of the truth for customers, products, and everything AI touches.
Modelling Data for AI/ML Features
Designing tables and stores that feed features, not just reports.
Normalization & Normal Forms
Removing redundancy deliberately, one normal form at a time.
NoSQL & Document Modelling
Designing schemas for systems that were built to bend.
Time-Series Modelling
Structuring data whose most important dimension is time itself.
Vector Embeddings & Vector Databases
The new modelling unit behind semantic search and RAG.