Orchestration turns a set of separate scripts into a dependable system.
Without it, jobs run on timers and hope that whatever they depend on has finished.
With it, each step knows what it needs and runs only when those inputs are ready.
Pipelines are described as a directed acyclic graph of tasks, the same shape as a causal DAG.
Each arrow means one task must finish before the next starts, and no loops are allowed.
The orchestrator schedules runs, such as every night at 2am, or triggers them when new data arrives.
Failed tasks are retried a set number of times before an alert goes out.
Every run is logged, with timings and outcomes, so slow or failing steps are easy to spot.
Backfilling reruns a pipeline for past dates, such as rebuilding last month after a bug fix.
Tasks should be idempotent, so retries and backfills give the same result as a single clean run.
Apache Airflow describes pipelines in Python code, which suits teams that already write Python.
Other tools, such as Dagster and Prefect, focus more on the data each task produces.
The orchestrator coordinates the work but usually leaves the heavy lifting to other systems, such as the warehouse.
In practice, a clear failure alert at 3am beats a wrong report discovered at 9am.
The usual mistake is running heavy data processing inside the orchestrator itself, which overloads it.