Industrial operations, IoT platforms, and software systems generate massive volumes of continuous, time-stamped data. To store and analyze this data efficiently, organizations typically rely on either data historians or time series databases (TSDBs).
Although the two technologies seem similar, they serve different roles — and choosing the wrong one creates bottlenecks in performance, scalability, and analytics.
This guide breaks down the key differences between a data historian vs a time series database, when to use each, and why modern industrial companies often need capabilities from both.
What Is a Data Historian?
A data historian is a specialized database designed to capture and store time-stamped operational data from industrial systems such as:
- PLCs
- SCADA
- MES
- Sensors
- Distributed control systems
- Industrial equipment
Data historians originated in manufacturing, oil & gas, utilities, and industrial automation. They typically provide:
- High-frequency ingestion of OT data
- Compressed storage of numerical time-series
- Simple trend analysis
- Dashboards and visualizations for plant operations\
- Integration with control systems and industrial protocols
Historians are built to run reliably on the plant floor, where stability, determinism, and OT/SCADA integration matter more than analytical flexibility.
What Is a Time Series Database (TSDB)?
A time series database is built to store and query large volumes of timestamped data from any source, not just OT systems. TSDBs are widely used in:
- IoT platforms
- Observability / monitoring
- Edge computing
- Smart devices
- Application performance tracking
- Energy, mobility, telecom, and cloud systems
A TSDB typically provides:
- Fast ingest of high-volume time-series
- Flexible querying
- Long-term retention
- Advanced analytics and aggregations
- Integration with cloud, data pipelines, machine learning, and AI
- Support for semi-structured data (JSON, geospatial, logs, metadata)
Where historians focus on OT operations, TSDBs enable real-time analytics, data science, and cloud-scale workloads.
Data Historian vs Time Series Database: Key Differences
Here is a detailed comparison to help clarify the differences.
1. Purpose
Data historian: Built for operational control, process monitoring, and plant-floor reliability.
TSDB: Built for large-scale analytics, cloud workloads, and flexible querying.
2. Data Types
Historian: Mostly numerical time-series from PLCs and SCADA.
TSDB: Supports structured, semi-structured, and unstructured data (JSON, logs, events, metadata).
3. Scale
Historian: Limited horizontal scaling and storage.
TSDB: Designed to scale out across clusters or cloud environments.
4. Analytics & AI
Historian: Trend analysis, basic querying, simple KPIs.
TSDB: Complex SQL analytics, joins, text search, vector search for anomaly detection, AI/ML workloads.
5. Integration
Historian: Deep OT integration, industrial protocols.
TSDB: IT-friendly, works with cloud, BI, AI, and data engineering tools.
6. Openness
Historian: Often proprietary, closed formats.
TSDB: Open standards, SQL interfaces, broad ecosystem.
7. Deployment
Historian: On-premise, often tied to equipment and SCADA systems.
TSDB: Edge, cloud, on-premise, hybrid.
Which One Do You Need? It Depends on the Use Case
Choose a Data Historian if you need:
- Tight integration with PLCs and SCADA
- Deterministic, stable data capture
- Short-term operational trends
- Plant monitoring dashboards
- Legacy industrial setups
Choose a Time Series Database if you need:
- Large-scale ingestion from IoT or cloud systems
- Complex analytics across years of data
- Integration with BI tools, AI models, and cloud platforms
- Fast ad-hoc queries
- Multi-model support beyond numerical time-series
Many organizations ultimately need both layers:
- A historian for operational control
- A TSDB for scalable analytics, AI, and long-term data retention
Modern Architectures Combine Both, This Is Where CrateDB Fits
Most industrial companies now move toward a hybrid architecture:
OT layer (SCADA, PLCs, MES) → Historian → Modern TSDB → Analytics / AI
CrateDB fits naturally as the modern time series database in this architecture. But unlike traditional TSDBs, CrateDB also delivers several historian-class capabilities:
- Very high ingestion throughput
- Automatic indexing with millisecond query availability
- Compressed time-series storage
- Real-time analytics and search
- Support for PLC/SCADA integration via partners
- Multi-model storage (JSON, logs, text, vectors, geospatial)
- SQL across all data types
This means CrateDB can act, depending on the customer environment, as both:
- A scalable modern historian, or
- A complement to an existing historian, or
- A long-term replacement for legacy historian architectures
Data Historian vs Time Series Database: Why Modern Teams Prefer a Unified Approach
A unified platform gives you:
- One place to store time-series + metadata + logs + contextual information
- Flexible SQL analytics on all industrial data
- Built-in readiness for AI, digital twins, predictive maintenance
- Cloud + edge support
- Long-term retention at low cost
- No need to maintain multiple disconnected systems
CrateDB enables this unified model without sacrificing performance or reliability.
FAQ: Data Historian vs Time Series Database
Is a historian the same as a time series database?
No. A historian is built for industrial control systems, while a TSDB is built for scalable analytics and flexible querying.
Can a TSDB replace a historian?
In some architectures, yes. Modern TSDBs like CrateDB provide both historian-like ingestion performance and advanced analytics.
Can I use both?
Many industrial companies use historians for OT and a TSDB for long-term analysis, forecasting, and AI workloads.
What’s the main advantage of a TSDB over a historian?
Flexibility, scalability, SQL, and ability to handle mixed data types.
Conclusion
When comparing data historian vs time series database, the decision comes down to use case:
- Use historians for operational reliability
- Use TSDBs for analytics, cloud, IoT, and AI
- Use both when running modern, data-driven industrial operations
