Abstract
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.
| Original language | English |
|---|---|
| Title of host publication | DATA ANALYTICS 2021 : The Tenth International Conference on Data Analytics |
| Editors | Sandjai Bhulai, Ivana Semanjski, Les Sztandera |
| Publisher | International Academy, Research, and Industry Association |
| Pages | 61-69 |
| Number of pages | 9 |
| ISBN (Print) | 978-1-61208-891-4 |
| Publication status | Published - Oct 2021 |
Fields of science
- 102001 Artificial intelligence
- 102010 Database systems
- 102014 Information design
- 102015 Information systems
- 102019 Machine learning
- 102022 Software development
- 102025 Distributed systems
- 102028 Knowledge engineering
- 102033 Data mining
- 102035 Data science
- 509018 Knowledge management
JKU Focus areas
- Digital Transformation