DataOps: Orchestrating Reliable ML Data Pipelines
DOI:
https://doi.org/10.15662/IJRAI.2021.0404001Keywords:
DataOps, ML Data Pipeline, Data Orchestration, Data Version Control, Observability, Statistical Process Control (SPC), Agile Data Engineering, Data Quality, Reproducibility, AutomationAbstract
The proliferation of Machine Learning (ML) models in production has elevated the criticality of managing data reliably throughout the ML lifecycle. DataOps has emerged as a disciplined practice combining Agile, DevOps, Lean, and statistical process control to enhance data pipeline reliability, speed, and governance. Originally coined in 2014 and gaining traction by 2017–2018, DataOps promotes automation, collaboration, monitoring, and versioning of data workflows across teams Wikipediadevopsschool.com. This paper presents an in-depth analysis of pre-2020 DataOps practices applied to ML data pipelines. We focus on DataOps’ integration of metadata, data version control, orchestration, observability, and quality checks to support reproducible and traceable data flows. Tools and patterns such as Apache Airflow for pipeline orchestration and the Stage–Transform–Consume pattern are discussed for orchestrating modular and stable data processing aycdata.comMedium. We also examine how statistical process control and monitoring reduce pipeline failures, and how version control frameworks borrowed from software engineering ensure auditability and reproducibility. The methodological framework blends literature review, case analysis, and synthesis of architectural patterns. This analysis underscores how DataOps transforms brittle ML pipelines into orchestrated, visible, and maintainable systems, and identifies current limitations and areas for further maturation before 2020.
References
1. L. Liebmann, “3 reasons why DataOps is essential for big data success,” IBM Big Data & Analytics Hub, June 19, 2014 Wikipedia.
2. Andy Palmer (Tamr), popularizing DataOps; Gartner Hype Cycle recognition, 2017–2018 Wikipedia.
3. Potel, R. (2019). A Real-Time Analytics Architecture for Enterprise Order Lifecycle Visibility and Backlog Management. International Journal of Research and Applied Innovations, 2(6), 2460-2469.
4. Hitachi Vantara, foundational definition and components of DataOps (Agile, DevOps, Lean foundations) Hitachi Vantara LLC.
5. Sugumar, R., Rengarajan, A., & Jayakumar, C. (2015). Design a Weight Based Sorting Distortion Algorithm for Privacy Preserving Data Mining. Middle-East Journal of Scientific Research, 23(3), 405-412.
6. Mathew, A. R., & Al Hajj, A. (2017). Secure communications on IoT and big data. Indian Journal of Science and Technology, 10(11).
7. Selvi, R., Saravan Kumar, S., & Suresh, A. (2014). An intelligent intrusion detection system using average manhattan distance-based decision tree. In Artificial Intelligence and Evolutionary Algorithms in Engineering Systems: Proceedings of ICAEES 2014, Volume 1 (pp. 205-212). New Delhi: Springer India.
8. Anbazhagan, R. S. K. (2016). A Proficient Two Level Security Contrivances for Storing Data in Cloud.
9. Jagadeesh, S., & Sugumar, R. (2017). Optimal knowledge extraction system based on GSA and AANN. International Journal of Control Theory and Applications, 10(12), 153–162.
10. Saravanan, C. B., & Sugumar, R. (2014, February). Nepotism responsive of data mining for prejudice inimitability. In International Conference on Information Communication and Embedded Systems (ICICES2014) (pp. 1-3). IEEE.
11. G. Vimal Raja, K. K. Sharma (2015). Applying Clustering technique on Climatic Data. Envirogeochimica Acta 2 (1):21-27.
12. Murugeshwari, B., Jayakumar, C., & Sarukesi, K. (2012). Secure Multi Party Computation Technique for Classification Rule Sharing. International Journal of Computer Applications, 55(7).
13. Sudhan, S. K. H. H., & Kumar, S. S. (2016). Gallant Use of Cloud by a Novel Framework of Encrypted Biometric Authentication and Multi Level Data Protection. Indian Journal of Science and Technology, 9, 44.
14. Anand, L., & Neelanarayanan, V. (2019). Feature Selection for Liver Disease using Particle Swarm Optimization Algorithm. International Journal of Recent Technology and Engineering (IJRTE), 8(3), 6434-6439.
15. Mathew, A., & Mai, C. (2018, May). Study of Various Data Recovery and Data Back Up Techniques in Cloud Computing & Their Comparison. In 2018 3rd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT) (pp. 2021-2024). IEEE.
16. G. Vimal Raja, K. K. Sharma (2014). Analysis and Processing of Climatic data using data mining techniques. Envirogeochimica Acta 1 (8):460-467.
17. Chiranjeevi, K. G., Latha, R., & Kumar, S. S. (2016). Enlarge Storing Concept in an Efficient Handoff Allocation during Travel by Time Based Algorithm. Indian Journal of Science and Technology, 9, 40.
18. Satyanarayana, D., Mathew, A. R., & Sathyashree, S. (2016). An Architecture for Wireless Communication Systems using Li-Fi technology. In 8th International Conference on Latest Trends in Engineering and Technology (ICLTET’2016) (pp. 37-41).
19. Sugumar, R., & Murugeshwari, B. (2016). An Efficient MChord based Authentication for Vehicular Ad-Hoc Networks.
20. Jeetha Lakshmi, P. S., Saravan Kumar, S., & Suresh, A. (2014). Intelligent Medical Diagnosis System Using Weighted Genetic and New Weighted Fuzzy C-Means Clustering Algorithm. In Artificial Intelligence and Evolutionary Algorithms in Engineering Systems: Proceedings of ICAEES 2014, Volume 1 (pp. 213-220). New Delhi: Springer India.
21. Raja, G. V. (2020). Metadata gets a makeover: The machine learning approach. International Journal of Computer Technology and Electronics Communication, 3(6), 2900-2903.
22. Socrates, S., Shanmugapriya, M., Murugeshwari, B., & Angalaeswari, S. (2024). Efficient Design for Implantable Device Constant Current Induction Doubly Fed Generating Incorporating Grid Connectivity. In Intelligent Solutions for Sustainable Power Grids (pp. 382-392). IGI Global Scientific Publishing.
23. Usha, G., Babu, M. R., & Kumar, S. S. (2017). Dynamic anomaly detection using cross layer security in MANET. Computers & Electrical Engineering, 59, 231-241.
24. Garg, V. K., Soundappan, S. J., & Kaur, E. M. (2020). Enhancement in intrusion detection system for WLAN using genetic algorithms. South Asian Research Journal of Engineering and Technology, 2(6), 62–64. https://doi.org/10.36346/sarjet.2020.v02i06.003
25. Pushparathi, V. G., Sudha, M., David, D. J., Anbazhagan, K., & Vethamani, S. E. (2020). A Continuous Decision Based Multi Kernel Median Filter for Noise Removal on Brain MRI Images. Advanced imaging, 1(3), 5.
26. Sudhan, S. K. H. H., & Kumar, S. S. (2015). An innovative proposal for secure cloud authentication using encrypted biometric authentication scheme. Indian journal of science and technology, 8(35), 1-5.
27. Santhoshini, G., & Anbazhagan, K. (2014, February). An object based software tool for software measurement. In International Conference on Information Communication and Embedded Systems (ICICES2014) (pp. 1-5). IEEE.
28. Sruthi, R. S., Ananya, S., & Murugeshwari, B. (2010). Web Based Virtual Control System Laboratory and On-Line Temperature Control of Electrophoresis Equipment using LabVIEW. International Journal of Computer Applications, 975, 8887.
29. Mathew A R, Al Zahli J A. Cloud Technology and the Challenges for Forensics InvestigatorsJ. DEStech Transactions on Computer Science and Engineering, 2017 (cnsce).
30. Saraswathi, U., Anbu, S., & Anbazhagan, K. (2014, February). Distributed file load rebalancing methodology for map reduce system. In International Conference on Information Communication and Embedded Systems (ICICES2014) (pp. 1-4). IEEE.
31. Natarajan, R., Sugumar, R., Mahendran, M., & Anbazhagan, K. (2012). Design a cryptographic approach for privacy preserving data mining. Int. J. Innov. Res. Sci. Eng. Technol, 1(1), 45-57.
32. Jagadeesh, S., & Sugumar, R. (2017). A Comparative study on Artificial Bee Colony with modified ABC algorithm. European Journal of Applied Sciences, 9(5), 243-248.
33. Soundappan, S. J. (2020). Big Data Analytics in Healthcare: Applications for Pandemic Forecastin. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 3(1), 2248-2253.
34. Padala, S. (2019). AWS Cloud Architecture for Scalable Healthcare Contact Centers. American International Journal of Computer Science and Technology, 1(2), 21-26.
35. Mallick, P. K., Satapathy, B. S., Mohanty, M. N., & Kumar, S. S. (2015, February). Intelligent technique for CT brain image segmentation. In 2015 2nd International Conference on Electronics and Communication Systems (ICECS) (pp. 1269-1277). IEEE.
36. Anbazhagan, K., SUGUMAR, D., Mahendran, M., & Natarajan, R. (2012). An efficient approach for statistical anonymization techniques for privacy preserving data mining. International Journal of Advanced Research in Computer and Communication Engineering, 1(7), 482-485.
