Comparative Review of Cloud Computing Platforms for Data Science Workflows

Abstract: With the advantages that cloud computing offers in terms of platform as a service, software as a service, and infrastructure as a service, data engineers and data scientists are able to leverage cloud computing for their ETL/ELT (extract, transform and load) and ML (machine learning) requirements and deployments. The proposed framework for the comparative review of cloud computing platforms for data science workflows uses an amalgamation of the analytical hierarchy process, Saaty's fundamental scale of absolute numbers, and a selection of relevant evaluation criteria (namely: automation, error handling, fault tolerance, performance quality, unit testing, data encryption, monitoring, role based access, security, availability, ease of use, integration and interoperability). The framework enables users to evaluate criteria pertaining to cloud platforms for data science workflows, and additionally is able to recommend which cloud platform would be suitable for the user based on the relative importance of the above criteria. Evaluations of the criteria are shown to be consistent and thus the weighting of criteria against the goal of cloud service provider or cloud platform selection are sensible. The proposed framework is robust enough to accommodate for changes in criteria and alternatives, depending on user cloud platform requirements and scope of cloud platform selection.