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IoT Sensor Telemetry (Synthetic) (Free Sample)
This is a free sample with 5,003 rows. The full dataset has 50,006 rows across 3 tables.
High-frequency telemetry from a simulated smart factory operating three CNC lines, a finishing cell, and a predictive-maintenance program over six months. Covers temperature, humidity, pressure, and vibration sensors sampled on a rolling 5-minute schedule with realistic shift patterns, machine assignments, maintenance alerts, and operational state changes.
Includes two injected anomalies: a progressive calibration drift on sensor 3 during month 4 and a sudden spike pattern on sensor 1 that signals an equipment failure event. Useful for time-series analytics, anomaly detection, edge telemetry pipelines, and Industry 4.0 monitoring demos.
Sample tables
| Table | Sample Rows |
|---|---|
| anomaly_events | 2 |
| sensors | 1 |
| telemetry_readings | 5,000 |
| Total | 5,003 |
Full dataset
The complete dataset includes all tables with full row counts:
| Table | Full Rows |
|---|---|
| anomaly_events | 2 |
| sensors | 4 |
| telemetry_readings | 50,000 |
| Total | 50,006 |
Formats included: CSV, Parquet, SQLite
Get the full dataset on Gumroad
About
Generated by Mindweave Technologies -- realistic synthetic datasets for developers, QA teams, and data engineers.
Every dataset features:
- Enforced foreign key relationships across all tables
- Realistic statistical distributions (not uniform random)
- Temporal patterns (seasonal, time-of-day, day-of-week)
- Injected anomalies for ML training and anomaly detection
- Deterministic generation (same seed = same output)
Browse all datasets: https://mindweavetech.gumroad.com
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