From the course: Knowledge Graph Data Engineering for Generative AI Use Cases
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Translating relational to graph data - Neo4j Tutorial
From the course: Knowledge Graph Data Engineering for Generative AI Use Cases
Translating relational to graph data
- [Instructor] Traditional ETL will have you load specific table columns into the corresponding database fields. For instance, the name column in raw customer data can map to the potential CusSat name field in your database schema. A knowledge graph is similar, except each row in a table is an instance or specific example of an entity. The container of these instances would be the node. For us, this is customer and purchase order, in this example. The biggest difference is that in a graph, the two nodes are related with a specific value called a relation or property. This relation explicitly connects the customer to the specific event, which is a purchase, and that purchase connects to what was purchased in that event, or a product. Connecting nodes is done through two specific graph elements. Attributes, which contain other nodes, such as State and are usually metadata associated with individual instances, or Properties. And these are the explicit relationships between tables or…
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