Building an Entire Graph Application with Nodestream
summary
A working task-dependency analytics pipeline is built end-to-end: Postgres-backed API emits Kafka events, Nodestream consumes them and maps data via YAML declarative mappings into Neo4j, no custom ETL code required. Migration-driven schema changes, checkpointing and migrations handle evolution and recovery. Demo runs Cypher queries for critical path and impact analysis (a delayed security audit blocks 11 tasks), and the composed Docker stack proves the graph replica and queries operate as advertised.
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chapters (15)
- 0:00Introduction and overview
- 1:55Why graphs solve problems
- 4:44What is Nodestream
- 7:34New features in Nodestream
- 9:21Road to Nodestream 1.0
- 10:05Building a real-world app
- 13:13Application architecture
- 14:39Demo setup and pipeline
- 16:02Data creation and sync
- 17:46Graph analysis insights
- 19:53Code walkthrough
- 21:57Graph queries vs SQL
- 23:01Nodestream configuration
- 24:06Flexibility and decoupling
- 24:48Q&A and closing
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