MessageRelay Digital Academy

Data · Intermediate

Airflow Orchestration Lab

Author DAGs, sensors, and SLAs with production-minded retry policies.

Move from cron scripts to observable workflows. You deploy Airflow locally, integrate with object storage triggers, and define SLAs mentors evaluate against realistic failure injections.

₩1,120,000 · 6 weeks · Live online

Request information Refund & Cancellation
Workflow nodes connected on a laptop screen during lab session

Included in this cohort

  • Astronomer-style project layout
  • TaskFlow API exercises
  • SLA and alerting configuration
  • Dynamic task mapping lab
  • Secrets handling patterns
  • Cross-DAG dependency module
  • Runbook writing template

Outcomes you can show

  1. Deploy a monitored DAG with backfill strategy
  2. Write an incident runbook for a failed sensor
  3. Compare Celery vs Kubernetes executors
Portrait of Tae-ho Kim

Mentor

Tae-ho Kim

Data platform SRE; automated nightly reconciliations for fintech pipelines.

Common questions

Yes. Legacy 1.x patterns are mentioned for maintenance contexts only.

Learner notes

"Anonymous cohort peer—our team adopted the SLA worksheet after week 3."

Client in logistics

"Dynamic mapping section finally clicked. Would have liked one more day on KubernetesPodOperator."