Build, deploy, and run data pipelines through an intuitive interface in minutes. Run at any scale instantly with Mage Pro.
Build, deploy, and run data pipelines through an intuitive interface in minutes. Run at any scale instantly with Mage Pro.
Now with Sonnet 3.7 and GPT-4.5 powering your pipelines*
Now with Sonnet 3.7 and GPT-4.5 powering your pipelines*
Move data from Google cloud to our analytics platform.
Remove any duplicate cells you find.
Done.Here’syourpipeline.
Write production-ready code faster with AI that understands your data. Get instant debugging, best practice recommendations, and automated data engineering tasks - all within your workflow.
Write production-ready code faster with AI that understands your data. Get instant debugging, best practice recommendations, and automated data engineering tasks - all within your workflow.
Write production-ready code faster with AI that understands your data. Get instant debugging, best practice recommendations, and automated data engineering tasks - all within your workflow.
Running
In
Out
Retriever.py
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from dense_retrieverimport DenseRetriever
classCustomRetriever(DenseRetriever):
def retrieve(self, query, documents):
# Custom retrieval logic
custom_results =super().retrieve(query, documents)
return custom_results
Build any pipeline type - batch, streaming, ML, RAG - in our interactive environment. Seamlessly combine Python, SQL, R, and dbt with instant previews at every step.
Build any pipeline type - batch, streaming, ML, RAG - in our interactive environment. Seamlessly combine Python, SQL, R, and dbt with instant previews at every step.
Build any pipeline type - batch, streaming, ML, RAG - in our interactive environment. Seamlessly combine Python, SQL, R, and dbt with instant previews at every step.
40
Template
8
Workflow
100
File
6
Pipeline
40
Team
Import
Mage breaks down complex data engineering tasks into smaller, manageable units called blocks. Each block in a pipeline is a standalone file that can be executed independently.
Mage breaks down complex data engineering tasks into smaller, manageable units called blocks. Each block in a pipeline is a standalone file that can be executed independently.
Mage breaks down complex data engineering tasks into smaller, manageable units called blocks. Each block in a pipeline is a standalone file that can be executed independently.
Deploy
Live
main
b83bf2 Optimized pipeline
Deploy
main
4h34xt Added dynamic blocks
Launch environments, collaborate in team workspaces, and deploy continuously with zero friction. Keep your pipelines organized and production-ready without the DevOps complexity.
Launch environments, collaborate in team workspaces, and deploy continuously with zero friction. Keep your pipelines organized and production-ready without the DevOps complexity.
Launch environments, collaborate in team workspaces, and deploy continuously with zero friction. Keep your pipelines organized and production-ready without the DevOps complexity.
Architecture
Scale from terabytes to thousands of concurrent tasks automatically. Mage’s intelligent architecture handles your most complex workloads without the operational overhead.
Scale from terabytes to thousands of concurrent tasks automatically. Mage’s intelligent architecture handles your most complex workloads without the operational overhead.
Scale from terabytes to thousands of concurrent tasks automatically. Mage’s intelligent architecture handles your most complex workloads without the operational overhead.
Engineering
Design
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Builder
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Comments
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EDITOR
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Triggers
ADMIN
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Monitoring
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Dashboard
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Customize Mage AI to your exact operational DNA through surgical-grade extensibility features — 100+ API endpoints, application behavioral hooks, and granular security permissions.
Customize Mage AI to your exact operational DNA through surgical-grade extensibility features — 100+ API endpoints, application behavioral hooks, and granular security permissions.
Customize Mage AI to your exact operational DNA through surgical-grade extensibility features — 100+ API endpoints, application behavioral hooks, and granular security permissions.
Batch
Integration
Streaming
RAG
Spark
ML
Batch
Integration
Streaming
RAG
Spark
ML
Familiar patterns.
Powerful new framework.
Build. Collaborate. Launch. Analyze. Win.
Filter
SQL
Transform along the way using
Py, R, or Sql
Amazon S3
SQL
Combine multiple sources
at different stages
Google Cloud
PY
Source data from anywhere
Aggregate
R
Refine and enhance
modularly and nondestructively
Daily Analytics
PY
Deliver exactly when and
where it is needed
Sensor
PY
Detect and react to data changes
You
Thomas
Sarah
OSS