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Time Series is a revolutionary way to store data.
It’s less about individual values, but values over time, looking for rates of change and other emerging patterns in your data.
An example is CPU statistics: when are they trending outside the norm so you can determine why performance lags are happening?
RavenDB gives you querying capabilities that match the way you would keep Time Series data. You also have notifications to alert you when the state of your system has changed.
You can plug Time Series values into machine learning models to track anomalies inside your data.
In this webinar, RavenDB CEO Oren Eini gives you a live example of Time Series data and machine learning solutions, demonstrating how you can monitor millions of independent data points to decipher the valuable messages your data is telling you.
You will see:
- How RavenDB compresses your data points, storing them in a very small amount of space
- How to utilize a distributed database cluster to collect data across thousands of sensors and then replicate them to each node to consolidate inside your nearest availability
- How to save even more memory with rollups that reduce your raw data into single points representing time intervals, like minutes or days, rather than each raw point
