Sure!
Stock market data is a good example of this. Suppose we are using intraday stock market data for CAT. Picking one source, the alphavantage api, we’d get multiple datapoints for each day, depending on the interval we selected. To ensure that all of the data are reasonable, I might set an expectation like expect_column_min_to_be_between("close", 200, 300). That way, I’d know that there were no anomalous closing prices in that day–I’m guarding against cases where the wrong ticker is reported, or I need to add logic for a stock split, for example.
If I wanted to get more sophisticated, I might use evaluation parameters to ensure that the min value for today is within some range of yesterday’s min value. In the OSS library there’s a fair amount of work to maintain a store of those historical metrics, but you can set up the expectation like this:
expect_column_min_to_be_between("close", {"$PARAMETER": "prev_min_close * 0.9"}, {"$PARAMETER": "prev_min_close * 1.1"})
In that case, you’d provide the previous close when you run the validation, and GX will validate that the minimum in the new dataset is within +/- 10%.