Time-series databases
Storage engines built for timestamped streams — millions of vitals per hour, compressed, with time-window queries that stay fast.
In one line
A time-series database (TSDB) optimises for one shape of data — (timestamp, source, value) at high rates — with time-based partitioning, heavy compression, and windowed aggregation as first-class operations.
How it works
Writes append into time-ordered chunks; old chunks compress (10–20× is normal) and age out by policy. Queries speak time natively: per-minute averages over six hours, downsampling for charts, continuous aggregates maintained as data arrives. Options range from Postgres-native (TimescaleDB — your relational data and telemetry in one database) to dedicated engines (InfluxDB, ClickHouse for analytics-heavy loads).
Where it shows up in digital health
Vital-sign histories from monitors and RPM (a 1 Hz feed is 86,400 rows per patient per day — regular tables suffer), ICU waveform archives, device-fleet metrics, and the storage behind every "trend" chart. Platform rule of thumb already in our docs: lab telemetry stays out of the main Postgres tables; a TSDB joins when a real stream does.