Time-Series Data Volume Calculator
Size a historian or telemetry platform from tag count, scan rate and retention
Inputs
Storage at full retention
4.8 TB
7 years online, including replication
Storage per year
705 GB
Raw samples per day
432.0M
Before compression, across all tags.
Ingest rate
5.0ksamples/s
Comfortable for a single well-tuned node
How it's calculated
Tags and scan rate
Samples per day
Compression and size
× retained × bytes
Storage
Per year and at retention
Raw samples per day = tags × (86,400 ÷ scan interval in seconds).
Stored volume = raw samples × compression retained × bytes per sample × replication factor, then multiplied out to the retention period.
Compression is the input that moves the answer most. Historians rarely store every sample: deadband and swinging-door algorithms discard points that sit on a straight line within tolerance, which on steady process signals can retain well under a tenth of the raw stream. Fast-moving or noisy signals compress far less, so it is worth sizing the noisy tags separately rather than applying one factor across the lot.
This estimates storage, not performance. Query patterns, downsampling strategy, partitioning and retention tiering decide whether the platform stays fast as it fills.
Why it matters
Getting this wrong is expensive in both directions. Undersize it and you are migrating a production historian a year in, or quietly dropping retention and losing the history that reliability analysis depends on. Oversize it and you have paid for storage and licensing you never needed.
The question worth asking early is what the data is actually for. Data kept for trending and troubleshooting can be aggressively compressed and downsampled after a few months. Data feeding condition monitoring, reliability analysis or a regulatory obligation usually cannot, because the value sits in the detail you would have thrown away. Deciding that per tag group, rather than platform-wide, is what keeps a platform both affordable and useful.
Related reading
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