Time-series data shows a pattern of change over time. For example, you might have a fleet of Internet of Things (IoT) devices that record environmental data through their sensors, as shown in the following example graph. This data could include temperature, pressure, humidity, and other environmental variables. Because each IoT device tracks these values over regular periods, your backend needs to ingest up to hundreds, thousands, or millions of events every second.
In this blog post, I explain how to optimize Amazon DynamoDB for high-volume, time-series data scenarios. I do this by using a design pattern powered by automation and serverless computing.