EdgeLake: Extending the Cloud to the Edge – an LF Edge Project
summary
EdgeLake claims to keep edge data in place and run SQL queries where the sensors live, returning aggregated results from distributed nodes (demo returns data from 10 nodes) instead of shipping raw streams to the cloud. Deployments run in hours or days on Linux with <500 MB footprint, local storage in Postgres or SQLite, and optional blockchain metadata or a master-node emulator. Sabetha, Kansas replaces cloud contracts within two weeks; APIs support REST, MQTT, Kafka and open integrations. Decentralized virtual layer map-reduce style query routing
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chapters (16)
- 0:00EdgeLake and LF Edge intro
- 1:07EdgeLake decentralized approach
- 3:17Edge data management problems
- 8:20EdgeLake city use case demo
- 13:25Query processing across nodes
- 17:03Federated learning with EdgeLake
- 20:02EdgeLake architecture
- 24:38Scalability in edge networks
- 31:05EdgeLake deployment
- 36:01Q&A data consistency
- 39:20Q&A trust and security
- 41:30Q&A performance and hardware
- 42:34Q&A security protocols
- 44:04Q&A platforms and databases
- 45:58Q&A connectivity formats
- 47:27Closing remarks
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