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Today, the digital marketing is constantly evolving, new tools are regularly introduced with the new consumer habits and the multiplication of data, often forcing marketers to delve into too much data that may not even give them the overview they need to make business decisions that have an impact. After the revolution of machine learning technology in other real world application, machine learning is changing the digital marketing landscape, 84% of marketing organizations are implementing or expanding their use of machine learning in 2018 [1].It becomes easier to predict and analyze consumer behavior with great accuracy. In our work we will start by establish an art of state on the main and most used machine learning potentials in digital marketing strategies and we show how machine learning tools can be used at large scale for marketing purposes by analyzing extremely large sets of data. The way that ML is integrated in digital marketing practices helps them better understand the target consumers and optimize their interactions with them.

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Introduction: This paper presents a dedicated machine learning model to predict the number of cases infected by the Corona Virus; the case of Morocco was chosen to validate this study. Case description: Completely realized in Spark ML with the 'Scala' language and tested for a certain number of algorithms generated on datasets coming from dedicated sources to gather Covid19 data in the world. Discussion and Evaluation: The results show the possibility of achieving better scores prediction after using the proposed method. We tested our model on the case of China and the results were relevant. Conclusion The proposed Machine Learning model can be applied to data from any country in the world. We have applied it in this paper to the case of Morocco and China. We are sending this work to the world to help them fight this 2019 Corona Virus pandemic.

Abstract-Processing a data stream in real time is a crucial issue for several applications, however processing a large amount of data from different sources, such as sensor networks, web traffic, social media, video streams and other sources, represents a huge challenge. The main problem is that the big data system is based on Hadoop technology, especially MapReduce for processing. This latter is a high scalability and fault tolerant framework. It also processes a large amount of data in batches and provides perception blast insight of older data, but it can only process a limited set of data. MapReduce is not appropriate for real time stream processing, and is very important to process data the moment they arrive at a fast response and a good decision making. Ergo the need for a new architecture that allows real-time data processing with high speed along with low latency. The major aim of the paper at hand is to give a clear survey of the different open sources technologies that exist for real-time data stream processing including their system architectures. We shall also provide a brand new architecture which is mainly based on previous comparisons of real-time processing powered with machine learning and storm technology.