Why You Need a Knowledge Graph And How to Build It
summary
Argues that a knowledge graph unifies scattered relational databases into one graph to answer cross-process questions (customer LTV, ad ROI, orders→deliveries) more easily than a dimensional warehouse; shows translating a third-normal-form e‑commerce schema into nodes/relationships, loading into Neo4j AuraDB, using Cypher merges with natural identifiers and daily refreshes, and orchestrating pipelines with Airflow+Python. Demonstrates a supply‑chain/marketing example that links campaigns to delayed deliveries and claims the integrated graph yields faster, clearer pattern and fraud detection than the warehouse.
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chapters (10)
- 0:00Agenda
- 1:10What is a Knowledge Graph
- 16:13Identify Data Sources
- 17:37Merge
- 18:37Relationships
- 19:15Node vs Property
- 19:53Example
- 20:43Data Warehouse
- 21:43transactional databases
- 22:20wrap up
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