In short, it lets you time-travel to retrieve prior states in tabular data. One useful example is continuous forecasting. Every forecast is done with a certain

In short, it lets you time-travel to retrieve prior states in tabular data. One useful example is continuous forecasting. Every forecast is done with a certain moving window of historical training data, and each time the forecast is run, this training data is different. In order to evaluate/backtest the performance of the forecasting algorithm over time, you need to be able to retrieve all the previous training datasets from the database, calculate a performance metric, and then aggregate. This lets you do that on a continuous basis in the database itself, without dumping each training set to secondary storage each time. Also, in many real life applications, historical data isn’t immutable — data corrections can arrive after the fact that will change state, so it’s usually not sufficient to just use a simple WHERE clause to retrieve a particular time range. This is where time travel becomes really useful.