# Jinja Templating and Macros — Apache Airflow: Orchestrate Data Pipelines

Source: https://www.geekswithgeeks.com/en/airflow/data-templating

> Use {{ ds }}, data_interval_start and params to make tasks depend on the run's date.

## The date belongs to the run

Many operator fields (such as `bash_command`, SQL files and paths) are **templated** with Jinja. Airflow supplies variables describing the run: `{{ ds }}` (logical date as YYYY-MM-DD), `{{ data_interval_start }}`, `{{ data_interval_end }}`, `{{ run_id }}`, `{{ params.x }}` and more. Use them instead of `datetime.now()` so that a re-run for a past day processes **that day's** data: the foundation of idempotent, backfill-able pipelines. In `@task` functions, receive the same values as arguments or from the context.

## A date-aware command and path

Re-running the run for 2026-09-15 reads and writes only partition `dt=2026-09-15`.

```python
export_orders = BashOperator(
    task_id="export_orders",
    bash_command=(
        "python export.py --from '{{ data_interval_start | ds }}' "
        "--to '{{ data_interval_end | ds }}' "
        "--out s3://lake/orders/dt={{ ds }}/"
    ),
)
```

## Never use now() for business dates

`datetime.now()` returns the time the task happens to execute, which changes on retries and backfills and silently produces wrong data. Use the run's own dates.

**Quiz:** What does `{{ ds }}` give you?

- [x] The logical date of the run as YYYY-MM-DD
- [ ] The current wall-clock time
- [ ] The DAG file name
- [ ] A random number

*Answer:* The logical date of the run as YYYY-MM-DD. It is tied to the run, so re-runs process the same date every time.