Managing IoT Storage with Google's Cloud Platform
summary
Claims capacity to ingest far beyond the oil‑field example (11.5 terabytes/day from 1.3M sensors) and demos that actually run: a Firestore demo with 40,000 simulated clients showing real-time reads/writes, a Bigtable GeoMesa pipeline for fleet geospatial queries, and BigQuery for ad‑hoc analytics. Pipeline concrete: Cloud Pub/Sub → Dataflow → Bigtable/BigQuery. Performance notes: Bigtable scales linearly per node, Spanner gives strong consistency for transactions, BI Engine reduces dashboard latency.
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chapters (23)
- 0:00Introduction
- 0:22Agenda
- 3:41IOT Core
- 6:26Data Models
- 8:52Distributed Systems
- 11:04Use Cases
- 13:36Mobile Apps
- 16:04Firestore Demo
- 19:05Fleet Monitoring
- 20:01Technical Requirements
- 20:24Database Service
- 20:42BigTable
- 21:11BigTable Examples
- 21:27BigTable No Sequel
- 23:03Apache HBase
- 23:45Geo Mesa
- 24:47Public Transit Cards
- 25:02Business Requirements
- 25:33Strong Consistency
- 27:44Transaction
- 27:59Analytics
- 29:31Timestep
- 31:39Summary
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