What is DAG-based Orchestration?

The Problem It Solves: Imagine you have 50 data tasks that must run every night. Task B needs data from Task A. Task C needs both A and B to finish. Task D can run in parallel with C. Managing this with cron jobs becomes a nightmare. If Task A fails at 2 AM, how do you prevent Task B from running on stale data? How do you retry just the failed parts? How do you track which tasks are stuck? DAG-based orchestrators like Apache Airflow and Prefect solve this by modeling your workflow as a graph. The "Directed" part means edges have direction (A flows to B). "Acyclic" means no loops (Task A cannot depend on Task C if Task C already depends on Task A). This guarantee prevents infinite execution cycles in production. How It Works: You define tasks and their dependencies in code. The orchestrator handles the execution mechanics: scheduling tasks when dependencies are met, retrying failed tasks (typically 3 to 5 attempts with exponential backoff), tracking state in a metadata database, and providing a UI to monitor progress. Consider a daily analytics pipeline. At 1:00 AM, the orchestrator triggers a DAG with 20 tasks. Three ingestion tasks run in parallel, pulling data from different APIs. When all three complete successfully, a transformation task processes the combined data. Finally, two tasks run in parallel: one publishes metrics to a dashboard, another trains a machine learning model. If the transformation task fails, the orchestrator automatically retries it without re-running the ingestion tasks. The Key Benefit: Separation of concerns. Your task code focuses on business logic ("transform this data"). The orchestrator handles coordination ("run this after that succeeds, retry on failure, alert on timeout"). This becomes critical when you scale from 20 tasks to 2,000 tasks across dozens of pipelines.