Recover failed pipelines automatically
Self-healing pipelines diagnose failures, apply safe fixes, and recover automatically.
Put your data pipelines on autopilot
Mage is an AI data engineering platform that runs production pipelines, checks data quality, fixes failures, and optimizes performance.
Keep production data pipelines reliable with automatic validation, recovery, and performance optimization
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Output rules verified
Catch data issues early
Automated data validation catches quality issues before they reach your dashboards, applications, and business teams.
Pipeline optimizationCalculate order totals
Applied automaticallyReplaced row-by-row processing
Before
df["total"] = df.apply(
lambda row: row["price"] * row["quantity"],
axis=1,
)Keep pipelines fast as your data grows
Mage identifies bottlenecks and automatically optimizes code and execution to keep your pipelines fast as your data grows.
Build data pipelines with AI, explore your data, create charts, and give agents reliable context
Describe what you need, and AI creates a scheduled data pipeline with code and tests.
Use cases
Turn manual data work into reliable automation
Start with the work your team repeats, and let Mage keep it running as your data and business needs change.
Automate data integration across your systems
Process vendor feeds and keep databases and business applications in sync with automated data pipelines.
Explore automationModernize legacy SQL and spreadsheet workflows
Turn existing SQL and spreadsheet logic into automated data models that adapt as source schemas change.
Explore data modelingGive AI agents accurate, up-to-date business data
Prepare and validate business data for AI agents, with current context tailored to the task each agent needs to do.
Explore AI-ready dataConnect your data sources and run SQL, Python, and dbt together
Use built-in or custom connectors to move data between your databases, warehouses, files, and business applications.
Customer metrics
Messy dataset
Marketing context
Product events
Billing records
Support notes
MageEnhancingContext created
Agents
Apps
Dashboards
APIs
Storage
Step 1
Connect data sources
Connect your databases and external APIs with built-in or custom data connectors.
Step 2
Run SQL, Python, and dbt
Schedule your existing code and manage dependencies in one workflow.
Step 3
Deliver data to your tools
Send data to warehouses, business applications, dashboards, and AI agents.
Run enterprise data pipelines with flexible deployment and access controls
Choose cloud or self-hosted deployment with isolated workspaces, role-based permissions, and expert support.
Hybrid cloud deployment
Workspaces
Role-based access
Dev
Staging
Production
Flexible deployment
Deploy pipelines where your data belongs
Run in the cloud, private cloud, or on premises to meet your infrastructure and security needs.
Enterprise controls
Control team access and production changes
Give teams isolated workspaces, set access by role, and review changes before production.
Expert support
Get expert guidance from migration onward
Plan your architecture, migrate data pipelines, and resolve issues with priority support.
