As AI and machine learning applications continue to proliferate, the data pipelines that power them have become more mission-critical than ever. As retrieval-augmented generation (RAG) applications and real-time AI systems are becoming the norm, any glitch in a data pipeline can lead to stale insights, suboptimal model performance, and inflated infrastructure costs.

Working in this domain has taught me that even minor lapses in pipeline reliability can escalate into major outages. To combat this, I rely on a framework I call the 4 R’s of pipeline reliability: robust architecture, resumability, recoverability, and redundancy. Here’s how each element contributes to building data systems that truly last.