# Pipeline Orchestration

# Pipeline Orchestration

Orchestrating data pipelines ensures that data flows reliably from sources into Curiosity Workspace, with proper error handling and monitoring.

# End-to-End Workflow

A typical orchestrated pipeline follows these stages:

1. Extraction: Pulling data from the source (DB, API, File System).
2. Transformation: Mapping and cleaning data to match Workspace schemas.
3. Loading: Ingesting data into the Workspace via the API.
4. Enrichment: Running NLP tasks or building additional graph links.
5. Validation: Verifying the integrity and completeness of the ingested data.

# Orchestration Tools

While Curiosity Workspace can handle simple scheduling, complex pipelines often use external orchestrators:

• Apache Airflow: For complex, multi-stage DAGs.
• GitHub Actions / GitLab CI: For triggering ingestion as part of CI/CD.
• Cron Jobs: For simple, periodic tasks.

# Best Practices

• Incremental Loads: Only process new or updated records to save time and resources.
• Error Handling: Implement robust logging and alerts for pipeline failures.
• Idempotency: Ensure that re-running a pipeline stage doesn't create duplicate data.
• Environment Separation: Test pipelines in Dev/Staging before running in Production.

# Monitoring and Alerting

Use the Workspace Monitoring tools and logs to track pipeline health. Set up alerts for:

• Sudden drops in ingestion volume.
• High error rates during loading or enrichment.
• Significant increases in pipeline execution time.