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 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
-
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
-
Set up a Mage Pro workspace
- Sign up at Mage Pro
- Connect a Git repo if needed for versioning and CI/CD
-
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
-
Configure scheduling or triggers
- Use streaming triggers (Kafka, webhooks) or batch schedulers (cron)
- Add event-based triggers for more control
-
Validate pipeline output
- Run pipelines in staging
- Preview outputs, compare with Estuary’s destination tables
- Verify schema integrity, incremental updates, and deduplication
-
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
🤖 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
- Open a Mage Pro pipeline and click “Ask AI”
- Paste in an Estuary flow JSON or describe your flow
- Ask: “Convert this Estuary flow into a Mage pipeline.”
- The AI will:
- Create Extract/Load blocks
- Generate SQL or Python for transforms
- Add scheduling and triggers
- 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