What is Pipeline Monitoring & Alerting?

The Core Problem: Data pipeline failures are often silent. Your web application still loads, users can browse products, and dashboards render without errors. But recommendations might be showing yesterday's data, A/B test results could be calculated on incomplete datasets, and finance reports might be off by 15 percent. Unlike API outages that trigger immediate user complaints, broken data pipelines can go undetected for hours or days. What You Actually Monitor: Monitoring covers two distinct dimensions. First is pipeline health: job success rates, run duration, resource consumption (CPU, memory), throughput in rows per second, and end to end latency. For example, a daily batch job might have an SLO requiring completion by 06:00 UTC with 99.9 percent success rate. Second is data quality and freshness: row counts compared to historical baselines, null rates in critical columns, schema changes, business constraint violations like negative prices, and how far behind real time your data is. A streaming events table might require freshness within 5 minutes for 99 percent of the day. Why Alerting Matters: Monitoring without alerting is just dashboards that nobody watches at 3 AM. Alerting translates threshold violations into actionable notifications. When your hourly user activity pipeline misses its 30 minute SLA, an alert fires to the on call engineer with context: which job failed, what the error was, and a link to the runbook. This minimizes Mean Time To Detect (MTTD) from hours to minutes.