Self-Healing Data Pipelines: Beyond Retries & Alerts скачать в хорошем качестве

Self-Healing Data Pipelines: Beyond Retries & Alerts

"Most data pipelines still work the same way they have for years: something breaks, an alert fires, and an engineer drops everything to fix it. Reactive, manual, and exhausting. In this episode, we explore self-healing data pipelines systems designed to expect failure, detect issues early, and recover intelligently before business users even notice. Our host is joined by Keshavi, an experienced data engineering leader, to cut through the hype and talk about what self-healing actually means in practice. Together, they break down: Why “self-healing” is more than just retries How Snowflake already supports resilient pipelines and where the gaps are The role of AI and Snowflake Cortex in adding assistive intelligence (not black-box automation) Why schema drift is one of the most critical failures to fix first What pipeline health really looks like in the real world The conversation stays grounded in real-world engineering, focused on reducing firefighting, improving reliability, and giving data teams back something invaluable: time (and sleep). Whether you’re building on Snowflake, experimenting with AI in data engineering, or just tired of late-night pipeline alerts, this episode offers practical insights you can apply today. Guest: Keshavi is a seasoned data engineering leader with experience delivering high-impact projects across Media & Entertainment and Healthcare. She’s known for building high-performing teams, strong delivery governance, and translating business vision into predictable, measurable outcomes. Host: Amirtha Varshini, Series Moderator, brings over 8 years of experience facilitating technically grounded conversations with data, analytics, and AI leaders, translating complex systems and insights into clear perspectives on enterprise decision-making."