Abstract
Big data analytics has recently emerged as an important research area due to the advent of user-generated content platforms. In recent years, we have witnessed an exponential increase in data production all over the world. This phenomenon is known as big data. Social media platforms, like Facebook, Twitter, and YouTube represent the most popular source of big data. Big data coming from social networking sites can be immensely useful for all companies by providing valuable insights on consumer preferences and changing trends. This information presents enormous opportunities for companies that have implemented Big Data management solutions. For this reason, how to store, manage, and transform social media posts into knowledge for decision-makers becomes an important research problem. On the other hand, the massive amount of data generated by users using social media platforms has led to the emergence and development of new technologies and techniques of data management. Data Lake is one of the latest technologies that was introduced to address this challenge in the last period. In this paper, we provide a NoSQL data lake design approach. More precisely, we start by introducing the main concepts of data lake. We also discuss the advantages of data lakes and their impact on big data analytics. Then, we describe some of the recent literature reviews on data lake design approaches. Finally, we provide a NoSQL Data lake using MongoDB that allows storing big data collected from social networks such as Facebook, Twitter, and Youtube.
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Dabbèchi, H., Haddar, N.Z., Elghazel, H., Haddar, K. (2021). NoSQL Data Lake: A Big Data Source from Social Media. In: Abraham, A., Hanne, T., Castillo, O., Gandhi, N., Nogueira Rios, T., Hong, TP. (eds) Hybrid Intelligent Systems. HIS 2020. Advances in Intelligent Systems and Computing, vol 1375. Springer, Cham. https://doi.org/10.1007/978-3-030-73050-5_10
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DOI: https://doi.org/10.1007/978-3-030-73050-5_10
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