Abstract
There are widespread and increasing interest in big data analytics (BDA) solutions to enable data collection, transformation, and predictive analyses. The development and operation of BDA application involve business innovation, advanced analytics and cutting-edge technologies which add new complexities to the traditional software development. Although there is a growing interest in BDA adoption, successful deployments are still scarce (a.k.a., the “Deployment Gap” phenomenon). This paper reports an empirical study on BDA deployment practices, techniques and tools in the industry from both the software architecture and data science perspectives to understand research challenges that emerge in this context. Our results suggest new research directions to be tackled by the software architecture community. In particular, competing architectural drivers, interoperability, and deployment procedures in the BDA field are still immature or have not been adopted in practice.
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Acknowledgment
This research is supported by Fulbright Colombia and the Center of Excellence and Appropriation in Big Data and Data Analytics (CAOBA), supported by the Ministry of Information Technologies and Telecommunications of the Republic of Colombia (MinTIC) through the Colombian Administrative Department of Science, Technology, and Innovation (COLCIENCIAS) within contract No. FP44842-anexo46-2015.
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Castellanos, C., Pérez, B., Varela, C.A., Villamil, M.d.P., Correal, D. (2019). A Survey on Big Data Analytics Solutions Deployment. In: Bures, T., Duchien, L., Inverardi, P. (eds) Software Architecture. ECSA 2019. Lecture Notes in Computer Science(), vol 11681. Springer, Cham. https://doi.org/10.1007/978-3-030-29983-5_13
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DOI: https://doi.org/10.1007/978-3-030-29983-5_13
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