II.
Tool JSON
Structured · livetool:airflow
Apache Airflow json
Inspect the normalized record payload exactly as the atlas UI reads it.
{
"id": "tool:airflow",
"_kind": "Tool",
"_file": "domain/tools/stack-part-implementations.yaml",
"_cluster": "domain",
"attributes": {
"displayName": "Apache Airflow",
"homepageUrl": "https://airflow.apache.org",
"kind": "orchestrator",
"description": "Author, schedule, and monitor data-pipeline DAGs. Python-native;\noperator ecosystem covers most data sources.\n"
},
"outgoingEdges": [
{
"from": "tool:airflow",
"to": "language:python",
"kind": "belongs_to_language"
},
{
"from": "tool:airflow",
"to": "stack-part:workflow-engine",
"kind": "implements_stack_part",
"attributes": {}
},
{
"from": "tool:airflow",
"to": "skill-area:event-driven-architecture",
"kind": "used_for",
"attributes": {}
},
{
"from": "tool:airflow",
"to": "tool:temporal",
"kind": "alternative_to",
"attributes": {
"comparison": "Workflow orchestration — Airflow is data-pipeline DAGs; Temporal is durable execution in code"
}
},
{
"from": "tool:airflow",
"to": "tool:dagster",
"kind": "alternative_to",
"attributes": {
"comparison": "Data orchestration — Airflow uses DAGs; Dagster is asset-centric"
}
},
{
"from": "tool:airflow",
"to": "tool:prefect",
"kind": "alternative_to",
"attributes": {
"comparison": "Data orchestration — Airflow uses DAGs; Prefect is Python-native"
}
},
{
"from": "tool:airflow",
"to": "tool:apache-beam",
"kind": "alternative_to",
"attributes": {
"comparison": "Data pipeline tools"
}
}
],
"incomingEdges": [
{
"from": "specialization:data-engineering-analytics",
"to": "tool:airflow",
"kind": "uses_tool"
},
{
"from": "stack-part:scheduler",
"to": "tool:airflow",
"kind": "implemented_by",
"attributes": {}
},
{
"from": "stack-part:workflow-engine",
"to": "tool:airflow",
"kind": "implemented_by",
"attributes": {}
},
{
"from": "stack-profile:data-lakehouse",
"to": "tool:airflow",
"kind": "composed_of"
},
{
"from": "stack-profile:batch-processing",
"to": "tool:airflow",
"kind": "composed_of"
},
{
"from": "stack-profile:data-warehouse-bi",
"to": "tool:airflow",
"kind": "composed_of"
},
{
"from": "stack-profile:data-quality-governance",
"to": "tool:airflow",
"kind": "composed_of"
},
{
"from": "stack-profile:master-data-management",
"to": "tool:airflow",
"kind": "composed_of"
},
{
"from": "stack-profile:data-pipeline-orchestration",
"to": "tool:airflow",
"kind": "composed_of"
},
{
"from": "stack-profile:etl-reverse-etl",
"to": "tool:airflow",
"kind": "composed_of"
},
{
"from": "stack-profile:data-lake-stack",
"to": "tool:airflow",
"kind": "composed_of"
},
{
"from": "tool:temporal",
"to": "tool:airflow",
"kind": "alternative_to",
"attributes": {
"comparison": "Workflow orchestration — Temporal is durable execution in code; Airflow is data-pipeline DAGs"
}
},
{
"from": "tool:dagster",
"to": "tool:airflow",
"kind": "alternative_to",
"attributes": {
"comparison": "Data orchestration — Dagster is asset-centric; Airflow is DAG-centric"
}
},
{
"from": "tool:prefect",
"to": "tool:airflow",
"kind": "alternative_to",
"attributes": {
"comparison": "Data orchestration — Prefect is Python-native; Airflow uses DAGs"
}
},
{
"from": "tool:apache-beam",
"to": "tool:airflow",
"kind": "alternative_to",
"attributes": {
"comparison": "Data pipeline tools"
}
}
]
}