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Interest in processing big data has increased rapidly to gain insights that can transform businesses, government policies, and research outcomes. This has led to advancement in communication, programming, and processing technologies, including cloud computing services and technologies such as Hadoop, Spark, and Storm. This trend also affects the needs of analytical applications, which are no longer monolithic but composed of several individual analytical steps running in the form of a workflow. These big data workflows are vastly different in nature from traditional workflows. Researchers are currently facing the challenge of how to orchestrate and manage the execution of such workflows. In this article, we discuss in detail orchestration requirements of these workflows as well as the challenges in achieving these requirements. We also survey current trends and research that supports orchestration of big data workflows and identify open research challenges to guide future developments in this area.
Barika et al. (2019) studied this question.