Industrial data is overwhelmingly time-series data - a pressure, a level, a flow rate, each with a timestamp, arriving forever. Storing billions of these points and querying them fast is a specialized problem, and the time-series database (TSDB) is the tool built for it. This guide explains what a TSDB is, why it beats a general-purpose database for sensor data, and how it underpins the modern SCADA historian.
What Is a Time-Series Database? in one line: A time-series database is a database optimized to store and query data points indexed by time - such as sensor readings that each carry a timestamp and a value. It is engineered for very high write rates, efficient compression of sequential timestamped data, and fast queries over time ranges.
Time-series data has a distinctive shape: it is append-heavy (new points constantly arrive, old points rarely change), it is queried by time range ('give me this tag for the last 30 days'), and it is enormous - a single field of instruments can generate millions of points a day, growing indefinitely. A general-purpose relational database can store this, but it was not designed for it, and at scale it struggles with ingest throughput and time-range query speed.
A TSDB is built around these characteristics. It indexes primarily on time, partitions data into time-based chunks so old data can be aged out or moved to cheaper storage, and assumes writes are mostly in timestamp order. This lets it ingest millions of points per second on modest hardware and answer range and aggregation queries - averages, min/max, downsampling - far faster than a row-oriented relational engine would.
The biggest win is compression. Because consecutive timestamps are regularly spaced and consecutive values often change little, a TSDB can store them extremely compactly using techniques like delta-of-delta encoding for timestamps and XOR encoding for values. Ratios of 10x or more over raw storage are common, which is what makes decades of high-resolution history economically storable.
TSDBs also provide first-class time functions: automatic downsampling and rollups (storing raw data at full resolution short-term and coarser aggregates long-term), retention policies that expire old data automatically, and windowed aggregations built into the query language. Well-known examples include InfluxDB, TimescaleDB (a PostgreSQL extension), and Prometheus; industrial historians use similar internal engines tuned for tag-based process data.
A SCADA historian is, at its core, a time-series database specialized for industrial tags. When a historian archives a tag's value on change, applies deadband compression, and lets you trend a tag over years, it is doing exactly what a TSDB does, wrapped in industrial features like tag metadata, engineering units, quality flags, and alarm history. The distinction is that a historian adds the operational context a bare TSDB lacks.
For a cloud SCADA serving a distributed oilfield, a scalable time-series store is what allows every tag from every well to be retained at full fidelity without a server room. Merobix stores telemetry from Modbus, DNP3, OPC UA, and MQTT sources in a time-series backend, so operators get long-term, queryable history of every monitored point as part of the platform rather than a separate historian project.
It stores and queries data indexed by time - sensor readings, metrics, and telemetry that each carry a timestamp. In industry it holds process history: pressures, levels, flows, and other tags, at high volume and over long periods, so you can trend and analyze them efficiently.
A relational database is general-purpose, optimized for related tables and transactional updates. A time-series database is specialized for append-heavy, timestamp-indexed data queried by time range. TSDBs offer far higher ingest rates, better compression of sequential data, and faster time-range queries for sensor data.
Essentially yes, with extras. A historian is a time-series database tuned for industrial tags, adding engineering units, data quality flags, tag metadata, deadband compression, and alarm history on top of the core job of storing and querying timestamped values efficiently.
This page references the protocol specifications published by the organizations below. Editions, product capabilities, and documentation change over time - confirm current requirements and specifications directly with the source.
Last reviewed: July 27, 2026. Merobix is not affiliated with, endorsed by, or sponsored by these organizations; their names are used only to identify the standards and products discussed.
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