A main research goal of IT nowadays is to investigate and design new scalable solutions for big data analysis. This goal asks for coupling scalable algorithms with high-performance programming tools and platforms. Addressing these challenges requires a seamless integration of the scalable computing techniques and big data analytics research approaches and frameworks. In fact, scalability is a key item for big data analysis and machine learning applications. Scalable big data analysis today can be achieved by parallel implementations that are able to exploit the computing and storage facilities of HPC systems and clouds, whereas in the next future exascale systems will be used to implement extreme scale data analysis. This chapter introduces and discusses cloud models that support the design and development of scalable data mining applications and report on challenges and issues to be addressed and solved for developing data analysis algorithms on extreme-scale systems.
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