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How we use data changes over time and across different scenarios. Take time-series data, for example. This type of data includes performance monitoring information, measurements from IoT sensors, streaming location data from mobile devices and other data that includes time as part of its unique identifying characteristics. Recently generated time-series data is especially useful when analyzing data streams for anomalies or sending a coupon to a mobile device in close proximity to a business. Data such as this can be highly valuable, at least for a short period of time. Sending an alert to an engineer about a disk that is about to run out of space is useful only if the message arrives before the impending event. Similarly, a coupon attracting a potential customer to your store is far less valuable if it arrives after the customer is back home. A time-series data store can be essential when creating statistical and machine learning models that detect anomalies. With enough data, algorithms can detect patterns that typically ...