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Spark & R Challenges and Opportunities

Legacy tools that were once only accessible to large corporations, are constantly being replaced by cost-effective and accessible open source platforms and programming languages, created and continuously improved by developer communities. As the Open Source solution for analytics and, more specifically, for predictive analytics and machine learning, Spark is becoming a popular and effective framework for resolving multifaceted commercial and statistical challenges.

Every day, new packages with new algorithms are added to the Spark framework and R portfolio (embedded in Spark), allowing any researcher, analyst or data specialist to find an analytical solution to a data processing issue, thus substantially reducing development time.

Challenges with Open Source

Although R interfaces and graphic tools eventually help developers create friendly user tools, developing these tools with R is not an easy or intuitive process. It requires developers with deep analytical understanding and special statistical expertise, and that naturally means more time to develop and deploy effective R-based analytical tools.

Challenges in Using Spark

Using Spark ML and R for development and deployment of big data machine learning solutions requires extensive technical and statistical expertise. As development work is done mainly by means of coding, it takes an extended period of time to develop end-2-end analytical processes and even longer times to deploy them in production.

Scalability Challenges

R holds temporary objects in virtual memory, which becomes rapidly saturated when dealing with large volumes of data. The challenges becomes even greater when trying to scale machine learning solutions to Big Data environments.

Furthermore, by operating the Spark engine, GSTAT BRAINs applications can run data management and machine learning processes on multi-nodes clusters in a manner that can meet any scalability challenge in term of data magnitude and parallel processing of thousands of analytical processes, promising superior performance.