Abstract: Data analytics workflows expressed through Directed Acyclic Graphs run commonly today in serverless computing environments such as Google Cloud Platform because these settings provide better scalability and efficient cost benefits. Serverless workflows help scientists and business professionals deal with complex analytics requirements across astronomy, science, and social science, as well as bioinformatics and neuroscience. The data pipelines require specific programming models and runtime environments to handle their complex requirements. This paper investigates execution methods for data analytics DAGs through Google Cloud Platform services. This research demonstrates both the designed architecture and the achieved results of workflow implementations that will provide instrument pipelines as a service.
Keywords: Data analytics, serverless computing, Directed Acyclic Graphs, Google Cloud Platform, Cloud Functions, data pipelines, workflow management.
Copyright Form