You can store process data in a regular database, so why do "historians" exist as a separate product category? The answer is scale and shape: industrial time-series data behaves very differently from the records a relational database was built for. This guide explains the difference and when each is the right tool.
The short answer: A process historian is a time-series database purpose-built to ingest and store streaming tag values (pressures, levels, flows) at high volume, with compression and fast time-range retrieval. A relational database is general-purpose, optimized for structured transactional records and complex queries, not billions of time-stamped samples.
Industrial data is a firehose: thousands of tags sampled every second, forever. Stored naively in a relational database, that is billions of rows, and writes and time-range queries bog down. Relational databases shine at structured, related records and complex joins - customers, orders, transactions - not at high-rate streaming time series.
A process historian is engineered for exactly this workload: extremely fast writes of incoming values, compression (often storing only meaningful changes rather than every identical reading), and fast retrieval of any tag over any time window for trending and reporting. That specialization is why historians are a distinct category, from the a traditional process historian at the high end to lighter time-series historians and the historians built into SCADA platforms. Modern general-purpose time-series databases (InfluxDB, TimescaleDB) blur the line but serve the same need.
| Dimension | Process Historian | Relational Database |
|---|---|---|
| Data shape | High-rate streaming time series | Structured, related records |
| Write pattern | Millions of fast appends | Transactional inserts/updates |
| Compression | Built-in (change-based) | Not time-series aware |
| Best at | Trending, reporting over time | Complex queries, joins, transactions |
A historian is a specialized time-series database optimized for high-rate streaming tag data - fast appends, compression, and fast time-range retrieval. A relational database is general-purpose, tuned for structured transactional records and complex queries, and struggles at industrial time-series scale.
For small scale, yes, but it degrades as tag counts and sampling rates grow into billions of rows. Purpose-built historians (or modern time-series databases) handle that volume far better with compression and time-optimized retrieval.
Closely related. A process historian is a time-series database tailored to industrial process data, often with SCADA integration and compression tuned for tags. General time-series databases (InfluxDB, TimescaleDB) serve a similar purpose more broadly.
Merobix includes a time-series historian on every plan - trends, reports, and compliance history, with no database to run.