Building Robust Data Pipelines: Best Practices for Error Handling, Monitoring, and Recovery
Year: 2025
Type: article
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.
Authors: Dharanidhar Vuppu, Mounica Achanta
Institution: SurveyMonkey (United States)
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4410464977