Apache Airflow orchestrates reliable data and ML pipelines with a declarative DAG model. We structure tasks for idempotency and retries so partial failures don’t cascade. Clear naming and docs make complex pipelines understandable.
We containerise tasks, add unit and integration tests, and deploy on Kubernetes for elastic capacity. Secrets are managed securely and connections are versioned. This turns Airflow into a stable backbone rather than a fragile cron replacement.
Custom plugins integrate lineage with DataHub or OpenLineage so governance teams see the full picture. Metrics and alerting surface lag and failures before they impact users. Your data platform becomes predictable and auditable.
