IOT & TELEMETRY
IoT & industrial telemetry
Millions of sensors, out-of-order arrivals, unbounded growth — handled by primitives built for exactly that shape of data.
THE STATUS QUO
Device data arrives late, duplicated, and forever. General-purpose databases buckle at the ingest rate; time-series silos cut you off from your reference data.
HOW EzraDB DOES IT
Device retries and at-least-once buses are deduplicated on DEDUP KEY (device_id, ts) — the table stays clean without app-side bookkeeping.
Align sensor A with the nearest reading from sensor B inside a tolerance window — the canonical sensor-fusion query, native in SQL.
Gorilla for floats, Delta for timestamps, RLE for states — sensor streams compress hard and scan fast.
Age raw readings out automatically while rollups stay hot; push cold history to object storage.
Reconstruct what the fleet looked like at the moment of an incident with AS OF SYSTEM TIME (row engine).
*In development — not yet production-ready.
IN PRACTICE
The sensor data stays queryable next to the reference data that gives it meaning.
SELECT q.ticker, q.ts, t.price
FROM quotes q
ASOF JOIN fills t
ON t.ticker = q.ticker AND t.ts >= q.ts -- most recent fill at/after each quote
ORDER BY q.ts;
MORE WAYS TEAMS USE EzraDB