Discovering DataOps: A Comprehensive Review of Definitions, Use Cases, and Tools

Data management approaches have changed drastically in the past few years due to improved data availability and increasing interest in data analysis (e.g., artificial intelligence). The volume, velocity, and variety of data requires novel and automated ways to "operate" this data. In accordance with software development, where DevOps is the de-facto standard to operate code, DataOps is an emerging approach advocated by practitioners to tackle data management challenges for analytics. In this paper, we uncover DataOps from the scientific perspective with a rigorous review of research and tools. As a result, we make the following three-fold contribution: we (1) outline definitions of DataOps and their ambiguities, (2) identify the extent to which DataOps covers different stages of the data lifecycle, and (3) provide a comprehensive overview on tools and their suitability for different stages of DataOps.