Airflow Cross-DAG Triggering → Orchestra

Overview

Airflow cross-DAG patterns — TriggerDagRunOperator, ExternalTaskSensor, SubDagOperator, Datasets — all have Orchestra equivalents. Orchestra provides two mechanisms:

TRIGGER_PIPELINE task — an explicit pipeline step that triggers another pipeline and waits for it to complete. Equivalent to TriggerDagRunOperator(wait_for_completion=True).
trigger_events: block — event-driven triggering at the pipeline root. When an upstream pipeline completes, this pipeline starts automatically. Equivalent to ExternalTaskSensor + DAG scheduling, but cleaner.

TriggerDagRunOperator → ORCHESTRA + TRIGGER_PIPELINE

# Airflow
trigger_downstream = TriggerDagRunOperator(
    task_id="trigger_reporting",
    trigger_dag_id="daily_reporting",
    conf={"date": "{{ ds }}", "env": "prod"},
    wait_for_completion=True,
    execution_date="{{ execution_date }}",
)

# Orchestra
task-001:
  integration: ORCHESTRA
  integration_job: TRIGGER_PIPELINE
  name: trigger_reporting
  parameters:
    pipeline_id: "uuid-of-daily-reporting-pipeline"   # UUID from Orchestra UI
    run_inputs:                                         # optional — equivalent to conf={}
      date: "${{ ORCHESTRA.CURRENT_TIME }}"
      env: prod
    branch: null                                        # optional — git branch override
  depends_on: []
  condition: null
  tags: []

TRIGGER_PIPELINE parameters:

Parameter	Required	Notes
`pipeline_id`	✅	UUID of the pipeline to trigger (find in Orchestra URL)
`run_inputs`	❌	Key-value dict passed as inputs to the triggered pipeline
`branch`	❌	Git branch to use for the triggered pipeline run

Key difference from Airflow: TRIGGER_PIPELINE always waits for the triggered pipeline to complete before proceeding. There is no wait_for_completion=False equivalent.

ExternalTaskSensor → `trigger_events:` (preferred)

When an upstream pipeline is in Orchestra and you want to start a pipeline automatically on its completion, use trigger_events: at the pipeline root — no sensor polling needed.

# Airflow
wait_for_elt = ExternalTaskSensor(
    task_id="wait_for_upstream_elt",
    external_dag_id="nightly_elt",
    external_task_id=None,   # wait for whole DAG
    poke_interval=60,
    timeout=3600,
)

# Orchestra — event-driven trigger
version: v1
name: daily-reporting

trigger_events:
  - type: pipeline
    pipeline_id: "uuid-of-nightly-elt-pipeline"
    statuses: [SUCCEEDED, WARNING]   # null defaults to SUCCEEDED + WARNING
    run_inputs:                       # optional inputs to pass to this pipeline run
      triggered_by: upstream_elt

pipeline:
  ...

TriggerEventModel fields:

Field	Required	Notes
`type`	✅	Always `pipeline`
`pipeline_id`	✅	UUID of the upstream pipeline, or `"*"` for any pipeline
`statuses`	❌	Which completion statuses trigger this pipeline. Default: `[SUCCEEDED, WARNING]`
`run_inputs`	❌	Input values to pass to the triggered run

Multiple upstream triggers (OR logic):

trigger_events:
  - type: pipeline
    pipeline_id: "uuid-of-pipeline-a"
  - type: pipeline
    pipeline_id: "uuid-of-pipeline-b"
  # fires when EITHER pipeline-a OR pipeline-b completes

SubDagOperator → Flatten or Separate Pipeline

Airflow Sub-DAGs are mostly an anti-pattern — they create scheduling complexity and have known issues. In Orchestra:

Option A — Flatten into stages: If the sub-DAG is small and tightly coupled to the parent, convert its tasks into additional stages in the same Orchestra pipeline.

Option B — Separate pipeline: If the sub-DAG represents a reusable or independently schedulable unit, convert it to its own Orchestra pipeline and trigger it with TRIGGER_PIPELINE.

# Airflow
subdag_op = SubDagOperator(
    task_id="process_subdag",
    subdag=create_subdag(dag_id, "process_subdag", default_args),
)

# Orchestra — Option B: separate pipeline + trigger
# In the parent pipeline:
task-trigger-subpipeline:
  integration: ORCHESTRA
  integration_job: TRIGGER_PIPELINE
  name: process_subpipeline
  parameters:
    pipeline_id: "uuid-of-process-pipeline"
  depends_on: []

Airflow Datasets → `trigger_events:` with any pipeline

Airflow Datasets (2.4+) trigger DAGs when an upstream DAG updates a dataset. In Orchestra, use trigger_events: with the relevant upstream pipeline UUID.

# Airflow
my_dataset = Dataset("s3://my-bucket/orders/")

with DAG("producer", schedule=None) as producer_dag:
    update_task = PythonOperator(..., outlets=[my_dataset])

with DAG("consumer", schedule=[my_dataset]) as consumer_dag:
    ...

# Orchestra — consumer pipeline
trigger_events:
  - type: pipeline
    pipeline_id: "uuid-of-producer-pipeline"
    statuses: [SUCCEEDED]

Before / After Example

Airflow DAG (before)

from airflow.operators.trigger_dagrun import TriggerDagRunOperator
from airflow.sensors.external_task import ExternalTaskSensor

with DAG("orchestrator") as dag:

    wait_for_elt = ExternalTaskSensor(
        task_id="wait_for_elt",
        external_dag_id="nightly_elt",
    )

    trigger_report = TriggerDagRunOperator(
        task_id="trigger_report",
        trigger_dag_id="daily_report",
        conf={"env": "prod"},
        wait_for_completion=True,
    )

    wait_for_elt >> trigger_report

Orchestra YAML (after)

# nightly-elt pipeline (separate YAML)
version: v1
name: nightly-elt
pipeline:
  ...   # existing ELT tasks

---
# orchestrator pipeline — triggered by nightly-elt completion
version: v1
name: orchestrator

trigger_events:
  - type: pipeline
    pipeline_id: "uuid-of-nightly-elt-pipeline"
    statuses: [SUCCEEDED, WARNING]

pipeline:
  stage-trigger-report:
    tasks:
      trigger-report:
        integration: ORCHESTRA
        integration_job: TRIGGER_PIPELINE
        name: trigger_daily_report
        parameters:
          pipeline_id: "uuid-of-daily-report-pipeline"
          run_inputs:
            env: prod
        depends_on: []
        condition: null
        tags: []
    depends_on: []

Gotchas

pipeline_id is a UUID — find it in the Orchestra UI pipeline URL (e.g. app.getorchestra.io/pipelines/xxxxxxxx-xxxx-...). It's not the pipeline name.
TRIGGER_PIPELINE always waits — unlike Airflow's wait_for_completion=False option, Orchestra always waits for the triggered pipeline before proceeding.
trigger_events: uses OR logic — any single entry firing starts the pipeline. For AND logic (wait for both pipeline A and pipeline B), chain: trigger_events fires pipeline B, and pipeline A is a TRIGGER_PIPELINE task at the start of pipeline B.
Sub-DAGs → prefer flattening — if the sub-DAG is <5 tasks, just add them as stages in the parent pipeline. The trigger overhead of a separate pipeline isn't worth it for small workloads.
statuses default — if omitted, trigger_events fires on SUCCEEDED and WARNING only. Explicitly set statuses: [SUCCEEDED] if you don't want WARNING runs to trigger downstream.
Circular dependencies — Orchestra validates that pipelines don't form circular trigger chains. Plan your pipeline graph before converting.

Adding Alerts

alerts:
  - name: on-failure
    statuses: [FAILED]
    destinations:
      - integration: SLACK
        destination: '#data-alerts'

References

Orchestra TRIGGER_PIPELINE: https://docs.getorchestra.io/docs/integrations/orchestra
Orchestra trigger_events: https://docs.getorchestra.io/docs/core-concepts/pipelines/schema

airflow-cross-dag-to-orchestra

Airflow Cross-DAG Triggering → Orchestra

Overview

TriggerDagRunOperator → ORCHESTRA + TRIGGER_PIPELINE

ExternalTaskSensor → `trigger_events:` (preferred)

SubDagOperator → Flatten or Separate Pipeline

Airflow Datasets → `trigger_events:` with any pipeline

Before / After Example

Airflow DAG (before)

Orchestra YAML (after)

Gotchas

Adding Alerts

References

Is this your skill?

airflow-cross-dag-to-orchestra

Airflow Cross-DAG Triggering → Orchestra

Overview

TriggerDagRunOperator → ORCHESTRA + TRIGGER_PIPELINE

ExternalTaskSensor → trigger_events: (preferred)

SubDagOperator → Flatten or Separate Pipeline

Airflow Datasets → trigger_events: with any pipeline

Before / After Example

Airflow DAG (before)

Orchestra YAML (after)

Gotchas

Adding Alerts

References

Is this your skill?

ExternalTaskSensor → `trigger_events:` (preferred)

Airflow Datasets → `trigger_events:` with any pipeline