What is a Time-Series Database?
A Time-Series Database (TSDB) is a database specifically optimized for time-series data—measurements that always consist of two parts:
- A timestamp (when was it measured?)
- A value (what was measured?)
Examples in production environments:
- Temperature, pressure, torque, spindle load at second or millisecond intervals
- Machine states (RUN/STOP/FAULT), counter values, cycles
- Energy and utilities consumption
- KPIs like OEE, throughput, or defect rate in short intervals
Unlike traditional SQL databases, a TSDB is designed to performantly store and analyze many measurement points at high frequency over long periods.
Why Do You Need a Time-Series Database on the Shop Floor?
In the digital shop floor context, three hard requirements converge:
- High Write Load: Many machines, sensors, and lines continuously write data
- Long History: Data should remain available for months/years for analysis, audits, and optimization
- Time-Axis Analysis: You want trends, progressions, comparisons ("today vs. last week," "before/after retrofit")—not just individual values
A Time-Series Database is designed exactly for this:
- Compression of time series (less storage, yet precise progressions)
- Fast queries over time windows ("last 24h," "shift 2," "batch 4711")
- Aggregations (min/max/averages, resampling to 1s/1min/5min) directly in the database
TSDB vs. Historian, MES & Traditional SQL
Process Data Historian: A historian is essentially a specialized time-series database with industrial focus (OT protocols, tag management, visualization). → Every historian engine is technically a TSDB, but with additional industrial functionality.
MES / Cloud MES: MES typically stores context-related events (order started, failure, defective part) and aggregated metrics. Raw process values are often kept in the historian/TSDB and connected as needed. → MES = "What happened, with which order/part?" → TSDB = "What did the process curves look like exactly, second by second?"
Relational SQL Database: Can theoretically store time series too, but scales significantly worse for industrial workloads (many tags, high frequency, long history) and requires extensive tuning.
Typical Use Cases for Time-Series Databases in Production
1. Process Monitoring & Troubleshooting
- Trend curves during failures: "What happened just before the breakdown?"
- Overlay of curves ("good" vs. "bad" cycles) for pattern recognition
2. OEE & Performance Analysis
- Derive load profiles, micro-stops, speed losses from signals
- Compare shifts/lines based on real running profiles
3. PAT & Predictive Quality
- Multivariate analysis on process curves (pressure/temperature progressions, force-displacement curves)
- Detect drifts and deviations before quality problems become visible
4. Predictive Maintenance
- Analysis of vibration, current, and temperature progressions to predict wear and failures
5. Energy & Resource Monitoring
- Time series of energy, compressed air, water, gas as foundation for energy efficiency projects and compliance reporting
How Does a TSDB Fit into Digital Factory Architecture?
Typical architecture:
- Shop Floor / OT: PLCs, sensors, robots, test stands → OPC UA, MQTT, fieldbus, gateways
- Time-Series Database / Historian:
- Captures high-frequency data
- Stores it compressed as time series
- Provides API/query interfaces
- MES / Cloud MES & Analytics:
- Uses aggregated metrics and selected signals for OEE, quality, maintenance, dashboards, alarms, reports
- Data science/BI tools access the TSDB directly or replicated data (data lake)
Result: Raw data remains in a specialized TSDB while MES and analytics work with processed data. This keeps the system performant and scalable—even when additional use cases (PAT, predictive, digital twin) are added later.