Building Robust Data Pipelines: Best Practices for Error Handling, Monitoring, and Recovery
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© 2025 by IJCTT Journal
Volume-73 Issue-4
Year of Publication : 2025
Authors : Dharanidhar Vuppu, Mounica Achanta
DOI :  10.14445/22312803/IJCTT-V73I4P120

How to Cite?

Dharanidhar Vuppu, Mounica Achanta, "Building Robust Data Pipelines: Best Practices for Error Handling, Monitoring, and Recovery," International Journal of Computer Trends and Technology, vol. 73, no. 4, pp. 140-148, 2025. Crossref, https://doi.org/10.14445/22312803/IJCTT-V73I4P120

Abstract
In today's data-driven world, businesses depend heavily on solid data pipelines to support everything from analytics and reporting to day-to-day decision-making. As data ecosystems scale in volume, velocity, and complexity, the role of the data engineer has evolved-from simply building pipelines to architecting resilient, observable, and recovery-aware systems. However, as data platforms grow more complex, the chances of something going wrong also increase. Whether it's a schema change, a broken upstream dependency, an infrastructure hiccup, or a resource crunch, pipeline failures are becoming more common - and when they happen, they can throw a wrench in operations and shake people's confidence in the data.In this paper, we highlight an important but often neglected area of data engineering: making sure pipelines can fail gracefully and recover without manual intervention. We'll dig into practical, real-world techniques for identifying and handling errors, setting up alerts and monitoring that actually matters, and building in automatic recovery using patterns that have stood the test of time. The goal is to give data engineers practical tools and approaches for creating pipelines that aren't just scalable but also resilient and self healing-so the data systems behind them stay reliable, even when things go wrong.

Keywords
Data Pipelines, Error Handling, Monitoring, Recovery, Resilience.

Reference

[1] Beth Plale, and Inna Kouper, “The Centrality of Data: Data Lifecycle and Data Pipelines,” Data Analytics For Intelligent Transportation Systems, pp. 91-111, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Aiswarya Raj et al., “Modelling Data Pipelines,” 46th Euromicro Conference on Software Engineering and Advanced Applications, Portoroz, Slovenia, pp. 13-20, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Vamsi Krishna Thatikonda, “Beyond the Buzz: A Journey through CI/CD Principles and Best Practices,” European Journal of Theoretical and Applied Sciences, vol. 1, no. 5, pp. 334-340, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Victor Chang, “Towards a Big Data System Disaster Recovery in a Private Cloud,” Ad Hoc Networks, vol. 35, pp. 65-82, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Santosh Kumar Singu, “Designing Scalable Data Engineering Pipelines Using Azure and Databricks,” ESP Journal of Engineering & Technology Advancements, pp. 176-187, 2021.
[Google Scholar] [Publisher Link]
[6] Nicolas Notario et al., “Integrating Privacy Best Practices Into a Privacy Engineering Methodology,” IEEE Security and Privacy Workshops, San Jose, CA, USA, pp. 151-158, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[7] J. Gray, and P. Shenoy, “Rules of Thumb in Data Engineering,” Proceedings of 16th International Conference on Data Engineering, San Diego, CA, USA, pp. 3-10, 2000.
[CrossRef] [Google Scholar] [Publisher Link]
[8] John Meehan, Nesime Tatbul, and Jiang Du, “Data Ingestion for the Connected World,” CIDR, pp. 1-11, 2017.
[Google Scholar] [Publisher Link]
[9] Dong Kyu Lee, “Data Transformation: A Focus on the Interpretation,” Korean Journal, vol. 73, no. 6, pp. 503-508, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Scott Haines, “Workflow Orchestration with Apache Airflow,” Modern Data Engineering with Apache Spark, pp. 255-295, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Khushmeet Singh, and Er Apoorva Jain, “Streamlined Data Quality and Validation using DBT,” International Journal of All Research Education & Scientific Methods, vol. 12, no. 12, pp. 4603-4617, 2024.
[Google Scholar] [Publisher Link]
[12] David Becker, Trish Dunn King, and Bill McMullen, “Big Data, Big Data Quality Problem,” IEEE International Conference on Big Data, Santa Clara, CA, USA, pp. 2644-2653, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[13] R.Y. Wang, H.B. Kon, and S.E. Madnick, “Data Quality Requirements Analysis and Modeling,” Proceedings of IEEE 9th International Conference on Data Engineering, Vienna, Austria, pp. 670-677, 1993.
[CrossRef] [Google Scholar] [Publisher Link]