Cascading

Cascading is a proven application development platform for building Big Data applications on Apache Hadoop. Whether solving simple or complex data problems, Cascading balances an optimal level of abstraction with the necessary degrees of freedom through a computation engine, systems integration framework, data processing and scheduling capabilities.

Uniquely, the platform offers Hadoop development teams portability. As new, more interesting, compute fabrics are developed, teams will need the ability to move existing applications without incurring the cost to rewrite them.

With Cascading, it is simply a matter of changing a few lines of code and a Cascading application is ported to another supported compute fabric. Today, Cascading applications run on and can be ported between MapReduce, Apache Tez and Apache Flink.With Cascading, developers can build and test their application locally, and then deploy them at scale in production.

The underlying query planner accounts for scale so that the applications will run properly even when encountering large data sets or small clusters. The platform allows developers to efficiently test code and process local files before deploying them on a cluster with Cascading’s local or in-memory mode. It also enables them to incorporate inline data assertions to define results at any point in their pipeline, where failed assertions are available for analysis.

The platform also offers users many benefits, and some of them are listed below as follows: Quick building of robust, reliable, data-oriented applications, Elimination of compute fabric lock-in, Development of testable and reusable integrations, data processing code and algorithms and Leveraging of existing best practices, skill sets and tools.

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