The Cascading processing model is based on a metaphor of pipes (data streams) and filters (data operations). Thus the Cascading API allows the developer to assemble pipe assemblies that split, merge, group, or join streams of data while applying operations to each data record or groups of records.

In Cascading, we call a data record a tuple, a simple chain of pipes without forks or merges a branch, an interconnected set of pipe branches a pipe assembly, and a series of tuples passing through a pipe branch or assembly a tuple stream.

Pipe assemblies are specified independently of the data source they are to process. So before a pipe assembly can be executed, it must be bound to taps, i.e., data sources and sinks. The result of binding one or more pipe assemblies to taps is a flow, which is executed on a computer or cluster using the Hadoop framework.

Multiple flows can be grouped together and executed as a single process. In this context, if one flow depends on the output of another, it is not executed until all of its data dependencies are satisfied. Such a collection of flows is called a cascade.