Graph Data Transformation: RDF, Property Graphs, and Neo4j

Graph databases are powerful, but moving data between different graph paradigms like RDF and property graphs can be challenging. Understanding how to bridge these two models is essential for modern data engineers and developers working with semantic web technologies. By taking this course, you will learn how to conceptualize, map, and execute transformations between RDF triple stores and property graph databases. You will gain a solid foundation in graph theory, learn to write federated queries, and build a practical migration pipeline using Elixir to load data into Neo4j. What you'll learn: - Understand the core differences between RDF semantic graphs and property graph models. - Write federated SPARQL queries to extract and merge data from multiple distributed endpoints. - Map RDF triples to Neo4j nodes, relationships, and properties. - Implement data transformation pipelines using Elixir to automate graph migrations. - Apply modern graph schema best practices to ensure data integrity and query performance. - Practice translating query patterns between SPARQL and Cypher. The course begins with foundational graph terminology and definitions, establishing a clear understanding of semantic web standards versus property graphs. You will then progress through step-by-step written guides and code examples to build your own functional migration pipeline. This text-only course is designed for software developers, data engineers, and database enthusiasts who are new to graph transformations. No advanced prior knowledge of Elixir or Neo4j is required, though a basic understanding of database concepts is helpful. Start reading today to master the art of graph-to-graph data integration.