RapidMiner Radoop easy to incorporate SparkR and PySpark for unified analytics
RapidMiner Radoop easy to incorporate SparkR and PySpark for unified analytics: RapidMiner Radoop, makes it easy to incorporate SparkR and PySpark scripts within a single, unified analytics workflow. Radoop, a core component of the RapidMiner platform, extends predictive analytics to Hadoop and Spark, giving organizations complete and holistic insight across all their data by leveraging the computation power of their Hadoop clusters. By adding SparkR and PySpark support, RapidMiner becomes the visual predictive analytics solution to combine data preparation on Spark, predictive analytics using Spark’s Machine Learning library (MLlib), and the ability to incorporate custom-built R and Python scripts. RapidMiner’s visual building block “language” makes the process of creating predictive analytics incredibly fast. Using Spark’s computation power makes analytics execution extremely efficient, empowering organizations to quickly and easily extract and operationalize the immense value deeply hidden within their Hadoop clusters.
“Hadoop is a very powerful technology, yet also a complicated environment, making it a challenge to tap its wealth of buried insights,” said Dr. Ingo Mierswa, Founder & CTO at RapidMiner. “Today’s announcement of Radoop v2.6 adding SparkR and PySpark support brings together customized data science scripts with powerful and standardized visual workflows, allowing businesses to create the most competitive predictive analytics.”
RapidMiner’s code-free software allows data scientists and business analysts use to quickly create predictive analytics processes to run within Hadoop and Spark.And to incorporate R, Python and SQL scripts within a code-free predictive analytics process.
RapidMiner Radoop, which uses built-in intelligence that automatically translates predictive analytics processes into the native languages of Hadoop and Spark. The new Hadoop connector allows users to extract data from Hadoop for in-memory rapid-prototyping of predictive analytic models. Spark’s Machine Learning Library (MLlib) allows to create and execute competitive predictive analytics and leverage Big Data. The offering with Cloudera, enables end users to effortlessly mine and model data—no coding required—mashup all data for a holistic business view, rapidly build predictive models to uncover opportunities and risks and operationalize predictive models by embedding them within business processes.
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More Information on Predictive Analysis Process
For more information of predictive analytics process, please review the overview of each components in the predictive analytics process: data collection (data mining), data analysis, statistical analysis, predictive modeling and predictive model deployment.