RDF and SPARQL Essentials

A practical introduction to RDF, Turtle, TriG and SPARQL for authoring and querying knowledge graph data

The way organisations store and connect data is changing fast. RDF and SPARQL put you at the leading edge of that shift.

Knowledge graphs are rapidly becoming a cornerstone of modern data architecture — and RDF and SPARQL are the two foundational technologies that make them work. RDF is the data model for representing information as richly described, explicitly linked networks. SPARQL is the query language for interrogating that data with precision. Together, they give you the building blocks to author, query and reason over knowledge graph data at scale.

While technologies like relational databases remain firmly part of the landscape, organisations are increasingly realising that their datasets need to be woven together across the data value stream — connected, self-descriptive and queryable in ways that traditional approaches simply weren't designed for. The result is the ability to answer complex business questions more smartly, intuitively and at scale. Sectors including IT, manufacturing, mass media, financial services and pharmaceuticals are already applying these technologies to tear down data silos, enrich their data architecture with semantics and drive next-generation AI and analytics — including grounding large language models over real enterprise data.

In this course, we're going to roll up our sleeves and get properly hands-on. You'll learn how to author RDF graphs using the Turtle and TriG formats — the most human-friendly ways of writing RDF data — and we'll spend a significant amount of time working through SPARQL together, building up from the essentials to solving real, meaningful querying problems along the way. The focus throughout is on applied, practical knowledge — not dry specification walkthroughs, but genuine problem-solving with tools you'll actually use.

What you will be able to do after this course:

• Author RDF graph data fluently in the Turtle and TriG formats

• Write SPARQL queries to read, filter, aggregate, update and delete knowledge graph data

• Use property paths to traverse graphs of arbitrary depth and complexity

• Combine graph patterns using unions, optionals, negation and grouping

• Build a rock-solid foundation for advancing into ontologies, RDFS, OWL and the wider Semantic Web Stack

Who this course is for:

This course is designed for data professionals — architects, engineers, analysts and anyone working at the intersection of data representation, data architecture and knowledge engineering — who want practical, hands-on fluency in RDF and SPARQL. No prior experience with semantic technologies is required, though a general familiarity with data concepts will help you get the most out of it. If you're serious about working with knowledge graphs, this is where that journey gets real.