Apache Hadoop is an open source technology that offers a radically cheaper alternative to the processing and storage of large amounts of unstructured data. Hadoop can be an uncomfortable fit within many IT environments. It challenges traditional approaches to data warehousing architecture, to the way in which IT projects are funded, and in some cases - can even threaten jobs.

Hadoop Development is one of the more popular technologies allowing large organizations to do queries to gleam intelligence about what their data says about their business or operations. It boasts strong performance, but is less expensive than legacy data analytics technologies because it can be distributed across commodity servers.

Hadoop can feel static for some businesses needing to crunch real-time data, because it relies on batch processing. It also requires training in how to use MapReduce, the algorithm behind Hadoop Development. 

Cascading is a widely used Java development framework that enables developers to quickly build workflows on Hadoop. 

Cascading Lingual is an ANSI SQL DSL. This means that developers can leverage existing ETL and SQL and migrate them to Hadoop. The deeper benefit is that applications designed for SQL can access data on Hadoop Development without modification using a standard database driver. 

MicroStrategy, SAS, R and SPSS: Cascading Pattern is a scoring engine for modeling applications. In a matter of hours, developers can test and score predictive models on Hadoop. 

Hadoop Development is radically different enough that some storage, server and data warehousing engineers will see a potential for the technology to obsolete their (vendor-specific) skills.