> ## 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 Estuary to Mage Pro

Estuary is great for real-time data capture—but teams often hit limits around transformation, orchestration, and control. **Mage Pro** offers an end-to-end solution for batch and streaming pipelines with **Python & SQL transformations**, full **orchestration**, and **AI-powered pipeline generation**.

## Why migrate from Estuary to Mage Pro?

Estuary specializes in real-time syncs with pre-built connectors. But it lacks flexibility for:

* Transforming data beyond the destination database
* Managing complex orchestration and dependencies
* Debugging and testing pipelines visually
* Git-native workflows and team collaboration
* Full control over schema evolution and failure handling

Mage Pro fills these gaps with **streaming + batch pipelines**, modular blocks for ETL, and full visibility into each step of your data flow.

***

## ✨ Mage Pro vs Estuary: Benefits Overview

| Capability                 | Estuary                 | Mage Pro                                  |
| -------------------------- | ----------------------- | ----------------------------------------- |
| **Streaming support**      | ✅ Native                | ✅ Native (Kafka, CDC, webhooks)           |
| **Batch processing**       | ⚠️ Limited              | ✅ Full batch support                      |
| **Transformations**        | ⚠️ Destination-only     | ✅ Python, SQL, AI-generated               |
| **Orchestration**          | ❌ None                  | ✅ Full DAG-level orchestration            |
| **Visual pipeline UI**     | ⚠️ Schema-level only    | ✅ Drag-and-drop builder                   |
| **AI assistance**          | ❌                       | ✅ Sidekick for code & pipeline generation |
| **Custom sources/targets** | ⚠️ Plugin SDK only      | ✅ Python-based SDK for APIs, files, DBs   |
| **Incremental loads**      | ✅ Supported             | ✅ Native with customizable logic          |
| **Git integration**        | ❌                       | ✅ Git-backed projects & CI/CD             |
| **Environments**           | ⚠️ Single workspace     | ✅ Dev, staging, and production            |
| **Observability**          | ⚠️ Basic logs           | ✅ Detailed logs, retries, lineage         |
| **Hosting**                | ✅ Cloud only            | ✅ Cloud or self-hosted                    |
| **Cost**                   | ⚠️ Event-volume pricing | ✅ Transparent usage-based plans           |

***

## 🛠️ Step-by-Step Migration Instructions

<Steps>
  1. **Audit your Estuary flows**
     * Export list of **flows** and their source → destination pairs
     * Note any field mappings or transformations
     * Identify real-time vs batch sync requirements

  2. **Set up a Mage Pro workspace**
     * Sign up at [Mage Pro](https://cloud.mage.ai)
     * Connect a Git repo if needed for versioning and CI/CD

  3. **Recreate flows as Mage pipelines**
     * Estuary source → **Source block** in Mage Streaming Pipeline
     * Estuary destination → **Destination block** in Mage Streaming Pipeline
     * Custom transforms → **SQL** or **Python blocks**
     * Choose between **streaming** (CDC, Kafka, webhook) or **batch**

  4. **Configure scheduling or triggers**
     * Use streaming triggers (Kafka, webhooks) or batch schedulers (cron)
     * Add event-based triggers for more control

  5. **Validate pipeline output**
     * Run pipelines in staging
     * Preview outputs, compare with Estuary’s destination tables
     * Verify schema integrity, incremental updates, and deduplication

  6. **Go live and monitor**
     * Enable alerts, retry logic, and audit logging
     * Use observability tools to track pipeline health and performance
     * Scale pipeline workers automatically via Kubernetes or ECS
</Steps>

***

## 🤖 Use AI Sidekick to Migrate Faster

Estuary flows are often schema-mapped and repetitive—perfect for AI Sidekick to help convert into Mage pipelines.

### 🔧 How to Use It

1. Open a Mage Pro pipeline and click **"Ask AI"**
2. Paste in an Estuary flow JSON or describe your flow
3. Ask: **"Convert this Estuary flow into a Mage pipeline."**
4. The AI will:
   * Create Extract/Load blocks
   * Generate SQL or Python for transforms
   * Add scheduling and triggers
5. Insert and run the pipeline directly in your workspace

***

## 🧠 Tips for Migrating Streaming Workflows

* Use **Kafka Extract blocks** or **webhook listeners** for streaming sources
* Combine **streaming + batch** in hybrid pipelines
* Use **block-level caching** to test transforms on historical data
* Handle schema evolution with **data validation and pipeline tests**

***

## ✅ After Migration: What You Gain

* One platform for **streaming + batch pipelines**
* Visual editor with modular, testable blocks
* AI-powered pipeline creation and debugging
* Git-native development & multi-environment deployments
* Better cost control and full observability
* Built-in orchestration and transformation logic

> Estuary handles streams. Mage Pro handles **everything**—with visibility, scalability, and flexibility built-in.

👉 [Start Migrating to Mage Pro](https://cloud.mage.ai)