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Cascading Pattern is a machine learning project within the Cascading development framework used to build enterprise data workflows. The Cascading framework provides an abstraction layer on top of Hadoop and other computing topologies. It allows enterprises to leverage existing skills and resources to build data processing applications on Apache Hadoop, without specialized Hadoop skills. Pattern, in particular, leverages an industry standard called Predictive Model Markup Language (PMML), which allows data scientists to leverage their favorite statistical & analytics tools such as R, Oracle, etc., to export predictive models and quickly run them on data sets stored in Hadoop. Pattern’s benefits include reduced development costs, time savings and reduced licensing issues at scale – all while leveraging Hadoop clusters, core competencies of analytics staff, and existing intellectual property in the predictive models.
Data Scientists or Data Analysts with intermediate experience with statistical modeling tools like SAS, R, Microstrategies and novice Java experience.