Graphing Your Way Through the Cosmos: Common Data Problems Solved with Graphs
summary
Shows that modelling business data as graphs avoids schema churn and messy join tables by actually demonstrating the work: a legacy table with 1,400 columns gets reshaped into nodes and edges, CRUD done with the Gremlin API against Azure Cosmos DB and visual traversals replace complex joins. Cosmos claims ~10 ms read/write SLA, global multi‑region replication, automatic indexing of every property, a free 5 GB/400 RU tier, and demos include HR and speaking‑engagement graphs plus Gremlin traversal examples that run live.
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chapters (26)
- 0:00Introduction
- 2:19About Chad
- 2:49What are graphs
- 4:40The history of graph theory
- 8:20Applications of graph theory
- 9:35What do graphs mean
- 10:44What is a graph database
- 12:32Power of graph databases
- 16:58Graph vs relational databases
- 24:10Company reorganization
- 27:40Corporate mergers
- 30:40Adding additional hierarchy
- 32:20Schema management
- 33:30What is Gremlin
- 35:10Gremlin Components
- 36:50Gremlin Support
- 37:30Azure Cosmos
- 48:23Graph Reversals
- 48:38Requirements
- 49:00Schema
- 49:22Query
- 49:58Document Schema
- 50:43Questions
- 51:45Querying Values
- 53:10Recap
- 54:15Conclusion
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