Many teams start with Apache Airflow for orchestration—but soon encounter challenges with scalability, debugging, and developer productivity. Mage Pro offers a modern alternative: visual pipelines, built-in observability, and AI-assisted development, with zero DevOps overhead.

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Why migrate from Airflow to Mage Pro?

Airflow is a powerful scheduler, but it wasn’t designed for:

  • Debugging pipelines visually
  • Mixing SQL and Python naturally
  • Streaming or event-driven workflows
  • Managing secrets, Git, or multi-tenant workspaces
  • AI-powered pipeline development

Mage Pro is built to solve these challenges out-of-the-box, with a modern developer experience and enterprise-grade scalability.

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✨ Mage Pro vs Airflow: Benefits Overview

Mage Pro goes far beyond orchestration. It’s a unified platform for data integration, SQL modeling (dbt-like blocks), AI-powered transformation, and streaming pipelines — all within a collaborative, Git-native workspace.

CapabilityAirflowMage Pro
Visual pipeline UI❌ Code-only (Jinja, Python)✅ Drag-and-drop editor
Built-in lineage⚠️ Plugin-based (e.g., OpenLineage)✅ Native, auto-generated
AI assistance❌✅ Generate, fix, and explain code with AI Sidekick
Multi-language support⚠️ Python only by default✅ SQL, Python, streaming, APIs
Data integration pipelines⚠️ Custom Python or 3rd-party plugins✅ Native connectors for databases, files, APIs
SQL block support⚠️ SQLOperator runs raw strings only✅ dbt-style SQL blocks with ref(), preview, and test support
Incremental modeling❌ (requires custom logic)✅ Native in SQL block config
Environment isolation⚠️ Requires Airflow deployments per team✅ Per-workspace configs, secrets, variables
Git integration⚠️ Manual DAG sync✅ Git-backed version control and CI/CD
Scheduling & triggers✅ Cron + sensors✅ Cron, events, file triggers, webhooks
Secrets management⚠️ Requires Vault or plugins✅ Built-in across workspaces and pipelines
RBAC & SSO⚠️ Manual plugins / external auth✅ Built-in, enterprise-ready
Observability & logging⚠️ Requires Prometheus, Grafana, etc.✅ Native UI for logs, metrics, traces
Scalability⚠️ Manual tuning of workers and queues✅ Auto-scaled executors on K8s or ECS
Streaming support❌✅ Kafka, CDC, real-time ingestion
Data previews & interactive runs❌✅ Preview data at every step
Team collaboration⚠️ Airflow not multi-tenant✅ Workspaces, permissions, activity logs

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🛠️ Step-by-Step Migration Instructions

  1. Inventory your Airflow DAGs

    • List your active DAGs and their task types:
      • PythonOperator, BashOperator, SQLOperator, etc.
    • Identify dependencies and scheduling logic

  2. Create a cluster in Mage Pro

    • Visit Mage Pro and sign in
    • (Optional) Connect your Git repo for version control

  3. Recreate your DAGs as Mage pipelines

    • Each Airflow DAG → 1 Mage pipeline
    • Each Airflow task → 1 Mage block:
      • PythonOperator → Python block
      • SQLOperator → SQL block
      • BranchOperator → Dynamic block
      • Sensor → Trigger block
    • Define dependencies visually using the UI

  4. Configure scheduling and triggers

    • Use cron, interval, or event-based triggers
    • Configure schedules via UI or YAML

  5. Run and validate pipelines

    • Use Mage Pro’s UI to test individual blocks
    • Preview dataframes, logs, and outputs
    • Compare results to your Airflow jobs

  6. Monitor, scale, and automate

    • Monitor pipeline runs and resource usage
    • Set alerts and retry rules
    • Mage Pro autoscaling handles executor resources automatically

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🤖 Convert Airflow DAGs with AI Sidekick

Skip the manual rewrites — Mage Pro’s AI Sidekick can automatically convert your Airflow DAG code into a Mage pipeline with just one prompt.

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🔧 How to Use It

  1. Click the “Ask AI” button in the top-right corner of the Mage Pro UI.
  2. Paste your Airflow DAG code — including @dag, @task, or PythonOperator-based workflows.
  3. Ask: “Convert this Airflow DAG to a Mage pipeline.”
  4. Sidekick will:
    • Parse the DAG structure and task relationships
    • Generate the corresponding Mage blocks (Python, SQL, dynamic, trigger)
    • Define block dependencies and scheduling logic
  5. Review and insert the generated pipeline directly into your project.

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💡 Why Use Sidekick?

  • Faster migration: turn entire DAGs into Mage pipelines in seconds
  • Less error-prone: Sidekick understands scheduling, dependencies, and operator types
  • Context-aware: uses your project setup and prior block structure
  • Fully editable: review, tweak, and insert blocks before saving

👉 Learn more in the AI Sidekick docs.

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🧠 Tips for Migrating Complex DAGs

  • Split large DAGs into smaller, modular pipelines
  • Use global variables or shared outputs to pass data between blocks
  • Replace Airflow SubDAGs with block groups
  • Use dynamic blocks to support branching and conditional logic

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✅ After Migration: What You Get with Mage Pro

  • AI-powered block generation and debugging (via AI Sidekick)
  • Visual pipeline builder and real-time logs
  • Git-backed version control and CI/CD
  • Built-in access controls, audit logs, and workspace isolation
  • Support for SQL, Python, streaming, dbt, Delta Lake, Iceberg, and more

Your pipelines, your logic—augmented by AI, automated for scale.

👉 Migrate to Mage Pro Today