Data plumbing without the
transforming and integrating data.
transforming and integrating data.
The modern replacement for Airflow.
replacement for Airflow
Open-source data pipeline tool for transforming and integrating data.
Give your data team magical powers
Effortlessly integrate and synchronize data from 3rd party sources.
Build real-time and batch pipelines to transform data using Python, SQL, and R.
Run, monitor, and orchestrate thousands of pipelines without losing sleep.
Easy developer experience
Start developing locally with a single command or launch a dev environment in your cloud using Terraform.
Language of choice
Write code in Python, SQL, or R in the same data pipeline for ultimate flexibility.
Engineering best practices built-in
Each step in your pipeline is a standalone file containing modular code that’s reusable and testable with data validations. No more DAGs with spaghetti code.
Immediately see results from your code’s output with an interactive notebook UI.
Data is a first-class citizen
Each block of code in your pipeline produces data that can be versioned, partitioned, and catalogued for future use.
Collaborate on cloud
Develop collaboratively on cloud resources, version control with Git, and test pipelines without waiting for an available shared staging environment.
Deploy Mage to AWS, GCP, Azure, or DigitalOcean with only 2 commands using maintained Terraform templates.
Scaling made simple
Transform very large datasets directly in your data warehouse or through a native integration with Spark.
Operationalize your pipelines with built-in monitoring, alerting, and observability through an intuitive UI.
You’ll love Mage. I bet Airflow gets dethroned by Mage next year!
Staff Data Engineer @ Airbnb
Awestruck when I used Mage for the first time. It’s super clean and user-friendly.
Senior Data Engineer @ Miniclip
One thing that hasn't been highlighted much about Mage is the community.
The slack channel has been great and not only did they help me with my immediate problems but they also took a SERIOUS look at my feature requests and included one of them in the latest release!
Principal Architect @ Red Alpha
I can say even after just trying it once, Mage would help any Data Engineering team write uniform, clean, well tested Data Pipelines. This is NOT something found in Airflow, Prefect, or Dagster.
Senior Data Engineer @ Rippleshot
The go to tool for any team looking to build and orchestrate data pipelines. Very friendly UI with a great developer experience, saving time in development.
Mage is going to be the clear winner in the data pipeline tooling space.
Senior Data Engineer @ ZebPay
I want to thank the Mage team for building such a great product. I am happy and excited to start using Mage as one of our daily data tools.
Lead Data Engineer @ Zubale
I just loved using it, so easy and intuitive to use.
Freelance Data Engineer
Probably will make people better programmers in general.
Machine Learning Engineer @ GroupBy Inc.
Give your data team
Questions & Answers
Who is the ideal user for this tool?
Our tool was built with data engineers and data scientists in mind, but is not limited to those roles. Other data professionals could find value in the tool.
How much does Mage cost?
Mage is free as long as you are self-hosted (AWS, GCP, Azure, or Digital Ocean).
How is Mage’s data pipeline engine software different from Airflow, etc?
Mage differentiates itself from Airflow and other tools based on 4 core design principles:
Focus on providing the easiest developer experience.
Ensuring engineering best practices are built-into every aspect of data pipelone building.
Everything in Mage is about data, that’s why data is a first-class citizen in Mage.
Scaling is made simple and possible without overhead of a large dedicated infra or DevOps team.