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The data management industry has matured over the last three decades, primarily based on Relational Data Base Management Systems (RDBMS) technology. The amount of data collected and analyzed in enterprises has increased several folds in volume, variety and velocity of generation and consumption, organizations have started struggling with architectural limitations of traditional RDBMS architecture. As a result a new class of systems had to be designed and implemented, giving rise to the new phenomenon of “Big Data”. The data-driven world has the potential to improve the efficiencies of enterprises and improve the quality of our lives. There are a number of challenges that must be addressed to allow us to exploit the full potential of Big Data. This article highlights the key technical challenges of Big Data.

Deep learning is certainly not a limited finding tactic, however, it follows several procedures and also topographies which could be associated with a huge speculum of complex problems. The strategy knows the illustrator in addition to differential attributes in a stratified way. Deep learning strategies have created a notable innovation along with sizable effectiveness in an assortment of applications with useful protection units. It is taken into account to become the greatest choice for revealing the architecture in high-dimensional relevant information by using a backpropagation process and concepts analysis. As deep learning has assisted help make significant advancements and also significant performance in many therapies, the wide-spread domain names of deep learning are service, scientific study, cancer cells diagnosis, natural language processing, medical diagnosis, handwriting recognition, trumpet call recognition, stock market study. This paper provides the intersections towards machine learning and also the challenges of deep learning.

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