FAQs - About time-series databases
At Timescale, we are dedicated to serving developers worldwide, enabling them to build exceptional data-driven products that measure everything that matters: software applications, industrial equipment, financial markets, blockchain activity, consumer behavior, machine learning models, climate change, and more. Analyzing this data across the time dimension ("time-series data") enables developers to understand what is happening right now, how that is changing, and why that is changing.
This might be measuring the temperature and humidity of soil, to help farmers combat climate change. Or measuring flight data to predict landing and arrival times for airlines and travelers. Or tracking every action that a user takes in an application, and the performance of the infrastructure underlying that application, to help resolve support issues and increase customer happiness. But these are just a few of the thousands of different ways developers are using time-series data to measure everything that matters today.
Time-series data is cropping up in more and more places: monitoring and DevOps, sensor data and IoT, financial data, logistics data, app usage data, and more. Often this data is high in volume and complex in nature (for example, multiple measurements and labels associated with a single time). This means that storing time-series data demands both scale and efficient complex queries. Yet achieving both of these properties has remained elusive. Users have typically been faced with the trade-off between the horizontally scalability of NoSQL and the query power of relational databases. We needed something that offered both, so we built it.
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