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Data: Apache Airflow Workflow Orchestration

Apache Airflow is the industry-standard workflow orchestration platform for data engineering pipelines, ETL processes, and scheduled automation tasks.

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Apache Airflow is the industry-standard workflow orchestration platform for data engineering pipelines, ETL processes, and scheduled automation tasks.

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content/modules/data-airflow-workflow-orchestration.md

Letzte Aktualisierung

18. März 2026

Kategorie

Datenintegration

Inhaltsprufsumme

e0241a0adb622a28

Tags

data-integration

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Diese Seite rendert das Markdown und Mermaid des Moduls direkt aus der offentlichen Dokumentationsquelle.

Overview#

Apache Airflow is the industry-standard workflow orchestration platform for data engineering pipelines, ETL processes, and scheduled automation tasks. Airflow DAGs (Directed Acyclic Graphs) define workflows as code -- each DAG is a Python script specifying tasks, their dependencies, execution schedule, retry policies, and alerting configuration. Argus integrates with Airflow to synchronise DAG inventory and execution status, giving operators and engineers visibility over the data pipelines that feed intelligence into and out of the Argus platform -- including threat feed ingestion schedules, STIX bundle generation jobs, report distribution tasks, and cross-system data synchronisation workflows.

Key Features#

DAG Inventory Synchronisation#

Sync the complete DAG inventory from an Airflow instance via 

syncAirflowDags
. Each DAG record captures DAG ID, description, schedule interval, current status (active/paused/failed), last execution timestamp, next scheduled execution, owner, and tag set. The inventory is persisted under organisation and clearance-level scoping.

DAG Status Monitoring#

DAG status tracks whether a DAG is actively scheduled (

active
), has been administratively paused (
paused
), or has entered a failure state (
failed
). Failed DAGs surface in Argus with the last execution error details, allowing SOC engineers and data platform teams to be alerted through the Argus notification pipeline rather than needing to monitor Airflow's own UI separately.

Execution History#

fetchDag
retrieves detailed execution history for a specific DAG, including per-run execution times, success/failure outcomes, and associated task instance states. This supports SLA monitoring -- if a critical threat feed ingestion DAG that should complete within 30 minutes starts consistently running for two hours, the degradation is visible before it affects the freshness of operational intelligence.

Schedule Transparency#

The schedule interval (cron expression or Airflow timed interval) is captured for each DAG, making it possible to verify that critical ingestion pipelines are running at the expected frequency. This matters for operational readiness -- a threat feed that was scheduled hourly but was accidentally changed to weekly is detectable immediately.

Operational Event Emission#

DAG sync uses 

emit_operational_entity
to surface relevant DAG state changes as operational events in the Argus event stream. A DAG transitioning from 
active
to 
failed
emits an operational alert, consistent with how other Argus integrations surface infrastructure health degradation.

Tag-Based Organisation#

Airflow DAGs are tagged by pipeline category (threat-intel, forensics, reporting, identity-sync, etc.). Tag filtering in Argus allows operators to view only the DAGs relevant to a specific mission area -- e.g., all threat intelligence ingestion pipelines for a morning handover check.

Use Cases#

  • Threat Feed Freshness Monitoring: MISP synchronisation, STIX TAXII polling, and MWDB ingestion all run as Airflow DAGs. Argus tracks their execution status -- if the overnight MISP sync DAG failed, analysts know before morning briefing that their indicator database may not be current.
  • Report Distribution Pipeline Oversight: Automated intelligence report generation and distribution runs as Airflow DAGs. Argus tracks delivery pipeline health, ensuring that scheduled reports actually reached their distribution lists on schedule.
  • Cross-System Synchronisation Health: Argus data synchronisation jobs (pushing enriched incidents to partner SIEMs, syncing case data to TheHive, pushing STIX to TAXII servers) run as Airflow DAGs. The Airflow integration gives operators a consolidated view of which cross-system sync jobs are healthy.
  • Exercise Preparation Automation: Before cyber exercises, data preparation pipelines (loading synthetic intelligence data, provisioning exercise user accounts, pre-positioning forensic artefacts) run as Airflow DAGs. Argus tracks completion of preparation pipelines as part of exercise go/no-go readiness checks.

Integration#

Available via GraphQL: 

airflowDags
, 
airflowStats
(queries); 
syncAirflowDags
, 
fetchAirflowDag
(mutations). All operations require authentication and organisation scoping.

Works alongside MISP (ingestion DAGs), STIX/TAXII (export DAGs), TheHive (case sync DAGs), SCIM Provisioning (identity lifecycle DAGs), and Sigma Rules (SIEM rule distribution DAGs). Airflow is the orchestration layer; Argus is the monitoring and visibility layer.

Last Reviewed: 2026-03-18