Graph Data Integration with the RDF Model

In an increasingly interconnected digital landscape, combining data from completely different sources is one of the biggest challenges in data engineering. The Resource Description Framework (RDF) provides a powerful, standardized way to model and integrate diverse data as a global graph. This text-only course guides you through the foundational concepts of the RDF model, showing you how to represent information as simple, flexible triples. You will learn to design extensible data structures, resolve semantic conflicts, and prepare datasets for seamless integration without relying on rigid relational schemas. What you'll learn: 1. Understand the core architecture of RDF, including subjects, predicates, objects, and namespaces. 2. Write and read standard serialization formats such as Turtle and JSON-LD. 3. Apply modern semantic web standards to link disparate data sources into a unified knowledge graph. 4. Practice querying graph data using SPARQL to extract precise insights. 5. Explore modern extensions like RDF-star for representing metadata about relationships. 6. Design clean schemas using basic RDF Schema vocabularies to establish taxonomies. You will start with core terminology, URIs, and triple structures, before advancing to modern serialization syntaxes and practical integration strategies through written examples and conceptual exercises. This course is designed for beginner data analysts, software developers, and database enthusiasts who want to learn graph modeling from scratch, with no prior experience in semantic technologies required. Start reading today to master the fundamentals of graph-based data integration.