> ## Documentation Index > Fetch the complete documentation index at: https://docs.mage.ai/llms.txt > Use this file to discover all available pages before exploring further. # Migrate from Airflow to Mage Pro 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. ## 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. *** ## ✨ 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. | Capability | Airflow | Mage Pro | | ------------------------------------ | ---------------------------------------- | ------------------------------------------------------------------------- | | **Visual pipeline UI** | ❌ Code-only (Jinja, Python) | ✅ Hybrid UI: drag-and-drop DAG builder + notebook-style coding experience | | **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 | *** ## 🛠️ 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](https://cloud.mage.ai) 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 *** ## 🤖 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. ### 🔧 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. ### 💡 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](/ai/sidekick). *** ## 🧠 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 *** ## ✅ 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](https://cloud.mage.ai)