A point-in-time correct join is a database operation that performs a join between two tables in a way that ensures the results reflect the state of the tables at a specific point in time.
When constructing a snapshot of data (e.g., training data or batch inference data) from precomputed features spread across different feature groups, we often need to construct a snapshot of feature values at a specific point in time. For example, a training dataset for supervised ML is a snapshot of feature values at the time of the observation of each label in each row in the training dataset.
A problem with creating a point-in-time-correct training data snapshot is that the underlying tables (feature groups) are typically updated at different cadences by different data pipelines. As such, it is not always possible to utilize an exact time-based join to obtain the desired result. The solution is a Point-in-Time correct Join that starts with the timestamps for the labels and retrieves the most recent feature values for the features from all the tables joined with the table containing the labels.