Augmentations are key components of the filters you write in DataSift.
We offer hundreds of targets and augmentations, and the number is growing. The augmentations documentation lists each augmentation, organized by the service which provides it, along with examples of how to use them.
Whereas targets contain information directly from a data source, augmentations provide additional data. For example, if you use the Salience augmentation alongside Twitter, Salience analyzes each tweet to determine the author's sentiment, positive or negative. This allows you to filter for all the negative sentiment about a competitor's product, for example, and use that to decide how yours could gain advantage.
Take a look at our Targets page too.