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Cascading is a feature rich API (application programming interface) for developing and implementing complicated and fault tolerant Big Data applications on Apache Hadoop. It defines, shares and executes data processing workflows in two ways. One is the single local node, cascading is used to test code and process local files. While testing it identifies and removes all errors being deployed on the cluster. The second step is the distributed computing cluster where cascading uses the Apache Hadoop platform adding an abstraction layer on the Hadoop API facilitating easy Hadoop application development job creation and job scheduling. Cascading supports even in Hadoop applications. 

Cascading has become a necessity because of the magnitude and complexities at which the data size has increased exceeding the processing capacity of the computing systems.  Developers have adopted the Apache Hadoop but find that building applications using Hadoop is not so simple. To simplify the issue, cascading has come into implementation. Another reason for the creation of cascading is its Global Namespace file system that provides a robust single storage framework

Cascading is an integral part of the java library and does not need to be set up separately. Cascading is compatible to JVM based languages like Scala, Jython, Jruby, Groovy and many more and the developers have successfully built many scripting and dynamic programming languages like the Lingual (ANSI SQL), Pattern(PMML), Scalding(Scala), cascalog (Clojure) and many more similar. It simplifies the process of building applications over Hadoop. Users can read and analyze large data sets on the home systems before executing them in a large production environment.
Cascading can create and build robust and reliable data-oriented applications and does away with the compute fabric lock-in. It develops data processing codes and algorithms that can be effectively implemented at any scale to get the maximum applications with the existing tools and practices. 

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