Cascading was developed to allow organizations to rapidly develop complex data processing applications. These applications come in two extremes.

On one hand, there is too much data for a single computing system to manage effectively. Developers have decided to adopt Apache Hadoop as the base computing infrastructure, but realize that developing reasonably useful applications on Hadoop is not trivial. Cascading eases the burden on developers by allowing them to rapidly create, refactor, test, and execute complex applications that scale linearly across a cluster of computers.

On the other hand, managers and developers realize the complexity of the processes in their data center is getting out of hand with one-off data-processing applications living wherever there is enough disk space or available CPU. Subsequently they have decided to adopt Apache Hadoop to gain access to its "Global Namespace" file system which allows for a single reliable storage framework. Cascading eases the learning curve for developers to convert their existing applications for execution on a Hadoop cluster. It further allows for developers to create reusable libraries and application for use by analysts who need to extract data from the Hadoop file system.

Cascading was designed to support three user roles. The application Executor, process Assembler, and the operation Developer.

The application Executor is someone, a developer or analyst, or some system (like a cron job) which runs a data processing application on a given cluster. This is typically done via the command line using a pre-packaged Java Jar file compiled against the Apache Hadoop and Cascading libraries. This application may accept command line parameters to customize it for an given execution and generally results in a set of data the user will export from the Hadoop file system for some specific purpose.

The process Assembler is someone who assembles data processing workflows into unique applications. This is generally a development task of chaining together operations that act on input data sets to produce one or more output data sets. This task can be done using the raw Java Cascading API or via a scripting language like Cascalog/Clojure, Groovy, JRuby, or Jython.

The operation Developer is someone who writes individual functions or operations, typically in Java, or reusable sub-assemblies that act on the data that pass through the data processing workflow. A simple example would be a parser that takes a string and converts it to an Integer. Operations are equivalent to Java functions in the sense that they take input arguments and return data. And they can execute at any granularity, simply parsing a string, or performing some complex routine on the argument data using third-party libraries.

All three roles can be a developer, but the API allows for a clean separation of responsibilities for larger organizations that need non-developers to run ad-hoc applications or build production processes on a Hadoop cluster.