Pipelines

In software delivery, a pipeline usually means CI/CD: when code is pushed, scripts build artifacts, run tests, scan for vulnerabilities, and deploy to staging or production. Each stage gates the next; failures stop the line and notify the team. Well-designed pipelines reduce manual release checklists and make rollbacks and feature flags easier to reason about.

In data engineering, pipelines describe ETL or ELT flows: extract data from sources, transform it (cleaning, joining, aggregating), and load it into a warehouse or lake for reporting and machine learning. Scheduling, idempotency, monitoring, and data quality checks matter as much as they do for deploy pipelines — bad data in production dashboards is a silent outage.

Whether code or data, the pattern is the same: define stages as code, version them, observe runs with logs and metrics, and iterate when bottlenecks appear. Pipelines are how reliable systems scale beyond what any single operator can run by hand.