Data management and Analytics for Industrial IOT
summary
Explains how an on‑prem OpenTSDB + HBase on-prem stack handles high throughput (around 10,500 writes/sec in the demo and ~408,894 rows written) and contrasts that with a cloud path using Azure Time Series Insights demo that shows hierarchy, marker-based anomaly tagging and near‑real‑time charts. Covers time‑series schema choices (tags vs fields, denormalize for analytics), edge vs cloud decision rules (latency vs batch ML), and a working end‑to‑end pipeline including ingestion, TSDB storage and dashboarding.
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chapters (15)
- 0:00Introduction
- 3:37Background
- 5:20Agenda
- 6:38Decision Making
- 13:50Operational Insights
- 18:11Monitoring
- 20:50Storage layer
- 26:41Time series data
- 29:40Irregular data
- 30:43Timeseries data
- 35:33Analytics
- 37:56Implementation
- 39:57Use Cases
- 41:15Architecture
- 42:41Azure Time Series
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