Alejandro Aboy outlines four levels of orchestration for data workflows, emphasizing that teams should match their orchestration approach to their project's maturity. Many tutorials jump straight to using Airflow, which can mislead newcomers into believing it's the only viable option. Instead, Aboy advocates for a more nuanced view that considers simpler alternatives based on specific needs. The article highlights the importance of understanding core orchestration concepts like scheduling, tasks, workflows, and dependencies before getting bogged down in tool specifics. The first level is Local Cron, ideal for proof-of-concept projects where simplicity is key. If version control and secret management are necessary, GitHub Actions combined with Docker becomes the next step. This setup is suitable for small daily tasks and simple workflows. As complexity grows, moving to AWS with ECR and Lambda is recommended, especially for teams already utilizing AWS infrastructure and needing serverless solutions. Finally, if a project demands intricate task dependencies and multiple workflows, tools like Prefect or Airflow are appropriate. Aboy stresses that most teams can stop at GitHub Actions, adding complexity only when genuine orchestration needs arise.