Xcom In Airflow [VERIFIED - 2026]

Here’s a structured, useful blog post about — written for data engineers who want to move beyond basic tasks and build real DAGs. Mastering XComs in Apache Airflow: Cross‑Task Communication Without the Pain One of the first surprises when learning Airflow is that tasks run isolated from each other. You can’t just set task_2.data = task_1.data . So how do you pass a value from one task to another? XComs .

@task def aggregate(results: list[str]): print(f"All results: results") xcom in airflow

def pull_function(**context): user_id = context['ti'].xcom_pull(task_ids='push_task', key='user_id') print(f"Received user_id") Here’s a structured, useful blog post about —

aggregate(download.expand(url=fetch_urls())) So how do you pass a value from one task to another

XComs are for coordination , not data transfer . Final Takeaway XComs are Airflow’s glue. They turn a set of isolated tasks into a coherent pipeline. Use them for small control signals, IDs, and results. Keep them light. And when you’re tempted to pass a big blob of data – stop, and ask yourself: should this be in object storage instead?

process_record(get_latest_record_id()) @task def produce_data(): return "ids": [1,2,3], "source": "api" @task def consume_one(data): return f"Got data['ids'][0]"