Developing Big Data in Computing Applications with Lambda Architecture - Thaha, M. Mohammed; Harun, Aizul Nahar; Deraman, Rafikullah; Jackulin, T.; T., Vignesh. (International Journal of Intelligent Systems and Applications in Engineering, 2023)
The IT industry and academics have both paid close attention to big data. Information generation and collection in the digital and computing worlds happen at a rate that quickly surpasses the limit. Globally, there are currently over 5 billion mobile phone owners and over 2 billion Internet users. There are projected to be 50 billion Internet-connected gadgets by 2020. At this point, data generation is predicted to increase 44 times over that of 2009. This application is distinct from hardware-centric evaluations in that it employs real-world data-intake scenarios to demonstrate the methodology’s effectiveness. This work adds to the corpus of prior research on LA while simultaneously addressing a significant gap in the field. This work establishes a standard for further research in this field by providing a fresh, empirically validated technique for assessing LA, a methodology that may be used in various big-data architectures. It also advances past work that lacked empirical validation. The field’s future research orientations are defined by the prospects and various unresolved difficulties in Big Data dominance. It is simpler to research the area and develop the most effective techniques for handling Big Data thanks to these lines of investigation.