Compared to Amazon RDS, Timescale Cloud is purpose built for massive streams of sensor data, helping you scale further, uncover insights faster, and focus more on end-user value.
Store billions of rows of sensor data in a platform purpose-built for time-series data.
Achieve high, stable ingest rates, thanks to custom indexing and automatic partitioning.
Store high-volume and high cardinality data with ease.
Store data only for the time you need with fine-grained data retention policies.
Uncover insights faster
Built-in analytical capabilities to help you gain critical insights from sensor data.
Quickly analyze real-time or historical sensor data with time-series SQL functions.
Millisecond response times for complex aggregate queries (e.g. querying devices by region, by type, by property, etc.) with continuous aggregates.
Power faster dashboards to save time and reduce friction for end-users.
Anybody can collect data points and put a line graph on a screen. That’s a solved problem. Your challenge is to develop all the context around the data, analyze it in that context, and present it to your users in the language and concepts they already know. Timescale can help you do that, by giving you more time to focus on end-user value, and less time focusing on things like ‘Can I connect tool x to my store?’ or ‘How am I going to scale this thing?
Nathan McMinn, CTO and co-founder at Conserv
Conserv is an preventative conservation platform that uses sensors to monitor risk factors and protect historical artifacts.