Raphtory + Apache Airflow

Orchestrate your graph intelligence pipelines at scale.

Schedule daily fraud detection, trigger alerts, and automate complex graph analytics workflows with the industry standard for orchestration.

Why Integrate?

Automation: Run hourly or daily graph analysis without manual intervention.
Dependency Management: Chain data loading, graph analysis, and alerting tasks.
Monitoring: Built-in tracking for pipeline health and failure alerts.
Scalability: Distribute graph workloads across workers using Celery or Kubernetes executors.

Setup

Example: Daily Fraud Ring Detection

1. Define Extraction Task

Load the latest transaction data from your source (e.g., Snowflake) and save it to temporary storage.

2. Define Analytics Task

Ingest the data into Raphtory and run temporal community detection.

3. Orchestrate the DAG

Link your tasks into a DAG that runs on a schedule.

Best Practices

Use XCom for Metadata: Pass counts and IDs via XCom, but keep the actual graph data in persistent storage (S3, GCS, or Delta).
Resource Limits: In multi-tenant Airflow environments, use Pools or strict worker memory limits for heavy graph tasks.
Idempotency: Ensure your graph analysis tasks can be retried safely without duplicating results in your dashboard tables.