Cascading was developed to allow organizations to rapidly develop complex data processing applications with Hadoop. The need for Cascading is typically driven by one of two cases:

Increasing data size exceeds the processing capacity of a single computing system. In response, developers may adopt Apache Hadoop as the base computing infrastructure, but discover that developing useful applications on Hadoop is not trivial. Cascading eases the burden on these developers and allows them to rapidly create, refactor, test, and execute complex applications that scale linearly across a cluster of computers.

Increasing process complexity in data centers results in one-off data-processing applications sprawling haphazardly onto any available disk space or CPU. Apache Hadoop solves the problem with its Global Namespace file system, which provides a single reliable storage framework. In this scenario, Cascading eases the learning curve for developers as they convert their existing applications for execution on a Hadoop cluster for its reliability and scalability. In addition, it lets developers create reusable libraries and applications for use by analysts, who use them to extract data from the Hadoop file system.

Since Cascading's creation, a number of Domain Specific Languages (DSL's) have emerged as query languages that wrap the Cascading API's, allowing developers and analysts to create ad-hoc queries for data mining and exploration. These DSL's coupled with Cascading local-mode allow users to rapidly query and analyze reasonably large datasets on their local systems before executing them at scale in a production environment. See the section on DSL's for references.

Cascading users typically fall into three roles:

The application Executor is a person (e.g., a developer or analyst) or process (e.g., a cron job) that 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. The application may accept command-line parameters to customize it for a given execution, and generally outputs a data set to be exported from the Hadoop file system for some specific purpose.

The process Assembler is a person who assembles data processing workflows into unique applications. This work is generally a development task that involves chaining together operations to act on one or more input data sets, producing one or more output data sets. This can be done with the raw Java Cascading API, or with a scripting language such as Scale, Clojure, Groovy, JRuby, or Jython (or by one of the DSL's implemented in these languages).

The operation Developer is a person who writes individual functions or operations (typically in Java) or reusable subassemblies that act on the data that passes 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, from simply parsing a string to performing complex procedures on the argument data using third-party libraries.

All three roles can be filled by a developer, but because Cascading supports a clean separation of these responsibilities, some organizations may choose to use non-developers to run ad-hoc applications or build production processes on a Hadoop cluster.