What are Graph Databases and Why should I care?
summary
Explains what graph databases actually do, contrasts RDF triple stores with property graphs, and shows real query examples where Cypher and Gremlin produce three to four lines that replace 12–15 line recursive SQL queries; demonstrates a recommendation and an org‑chart traversal that run faster and are easier to maintain. Names concrete products like Neo4j, DataStax/JanusGraph and Blazegraph/Dgraph, notes DataStax and Neo4j handle partitioning/clustering and JanusGraph joins the Linux Foundation.
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chapters (26)
- 0:00Intro
- 2:45History of Graph Theory
- 5:04Things Connected to Similar Things
- 7:13A Typical Graph Query
- 8:04Through Arbitrary Hops
- 11:01Common Graph Problems
- 12:59Dependencies - Pipelines
- 13:23Clustering - Industrial Assets
- 14:04Similarity - Social Networks/Fraud
- 14:44Categorization. Recommendation
- 17:50Interactions Are Graphs
- 19:00What Industries Use Graphs?
- 22:21What is a Graph Database
- 23:09Database types
- 25:43Graph Databases are fastest growing datastore type
- 26:32Database Versus Framework
- 27:56Graph Processing Frameworks
- 31:15RDF Versus Property Example
- 35:04Property Graph Datastores
- 37:14Multi-Model Datastores
- 39:07Querying your data
- 41:42Example Property Model Queries
- 45:13Visualizing my Data
- 46:15Visualization Examples
- 47:46Product Recommendation
- 51:57Getting Started
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