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.
A data historian is a specialized database designed to capture and store time-stamped operational data from industrial systems such as:
Data historians originated in manufacturing, oil & gas, utilities, and industrial automation. They typically provide:
Historians are built to run reliably on the plant floor, where stability, determinism, and OT/SCADA integration matter more than analytical flexibility.
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:
A TSDB typically provides:
Where historians focus on OT operations, TSDBs enable real-time analytics, data science, and cloud-scale workloads.
Here is a detailed comparison to help clarify the differences.
Data historian: Built for operational control, process monitoring, and plant-floor reliability.
TSDB: Built for large-scale analytics, cloud workloads, and flexible querying.
Historian: Mostly numerical time-series from PLCs and SCADA.
TSDB: Supports structured, semi-structured, and unstructured data (JSON, logs, events, metadata).
Historian: Limited horizontal scaling and storage.
TSDB: Designed to scale out across clusters or cloud environments.
Historian: Trend analysis, basic querying, simple KPIs.
TSDB: Complex SQL analytics, joins, text search, vector search for anomaly detection, AI/ML workloads.
Historian: Deep OT integration, industrial protocols.
TSDB: IT-friendly, works with cloud, BI, AI, and data engineering tools.
Historian: Often proprietary, closed formats.
TSDB: Open standards, SQL interfaces, broad ecosystem.
Historian: On-premise, often tied to equipment and SCADA systems.
TSDB: Edge, cloud, on-premise, hybrid.
Choose a Data Historian if you need:
Choose a Time Series Database if you need:
Many organizations ultimately need both layers:
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:
This means CrateDB can act, depending on the customer environment, as both:
A unified platform gives you:
CrateDB enables this unified model without sacrificing performance or reliability.
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.
When comparing data historian vs time series database, the decision comes down to use case: