Submission declined on 24 November 2025 by Niafied (talk). This draft reads like an advertisement. Wikipedia is an encyclopedia, not a platform for promotion or marketing. Drafts that are exclusively promotional may be deleted without notice. Wikipedia articles must be written neutrally in a formal, impersonal, and dispassionate way. They should not read like a blog post, advertisement, or fan page. Rewrite the draft to remove: promotional language: see Words to watch; personal commentary: opinions or direct addresses to the reader; informal language. Instead, only summarize in your own words a range of independent, reliable, published sources that discuss the subject. If you have a conflict of interest (e.g. you are the subject, an employee, or a relative) or are being paid to edit, you must disclose this to comply with Wikipedia's Terms of Use. If you would like to continue working on the submission, click on the "Edit" tab at the top of the window. If you have not resolved the issues listed above, your draft will be declined again and potentially deleted. If you need extra help, please ask us a question at the AfC Help Desk or get live help from experienced editors. Please do not remove reviewer comments or this notice until the submission is accepted. Where to get help If you need help editing or submitting your draft, please ask us a question at the AfC Help Desk or get live help from experienced editors. These venues are only for help with editing and the submission process, not to get reviews. If you need feedback on your draft, or if the review is taking a lot of time, you can try asking for help on the talk page of a relevant WikiProject. Some WikiProjects are more active than others so a speedy reply is not guaranteed. How to improve a draft Wikipedia:Contributing to Wikipedia – a basic overview on how to edit Wikipedia. Help:Wikitext – how to use the markup Help:Referencing for beginners – how to include references Wikipedia:Article development – how to develop your article Wikipedia:Writing better articles – how to improve your article Wikipedia:Verifiability – make sure your article includes reliable third-party sources You can also browse Wikipedia:Featured articles and Wikipedia:Good articles to find examples of Wikipedia's best writing on topics similar to your proposed article. Improving your odds of a speedy review To improve your odds of a faster review, tag your draft with relevant WikiProject tags using the button below. This will let reviewers know a new draft has been submitted in their area of interest. For instance, if you wrote about a female astronomer, you would want to add the Biography, Astronomy, and Women scientists tags. Add tags to your draft Editor resources Easy tools: Citation bot (help) | Advanced: Fix bare URLs Declined by Niafied 6 months ago. Last edited by OpalYosutebito 28 days ago. Reviewer: Inform author. Resubmit Please note that if the issues are not fixed, the draft will be declined again.

{{subst:AfC submission/col|paid}}

A major contributor to this Mage Technologies appears to have a close connection with its subject. It may require cleanup to comply with Wikipedia's content policies, particularly neutral point of view. Please discuss further on the talk page. See our advice if the article is about you and read our scam warning in case someone asks for money to edit this article. (Learn how and when to remove this message)

Mage (stylized as mage.ai) is an open-source data orchestration platform that enables data engineers to build, run, and manage data pipelines for integrating and transforming data.^[1] Founded by former Airbnb engineers in 2020, the platform has been adopted as an alternative to Apache Airflow, emphasizing developer experience and ease of use.^[2]

Mage was founded in late 2020 by Tommy Dang and Xiaoyou Wang, both former engineers from Airbnb.^[3][4] Dang spent over five years at Airbnb working as a software engineer and data engineer, where he developed data tools for product developers and machine learning tools.^[5] The experience of building internal tools at Airbnb directly influenced the creation of Mage, as Dang recognized the need for better data pipeline tools that didn't require extensive infrastructure management.^[6]

In October 2021, the company raised $6.3 million in seed funding led by Gradient Ventures, Google's AI-focused venture fund, with participation from Neo, Designer Fund, and angel investors including Unity CEO John Riccitiello and Behance founder Scott Belsky.^[3][7][8]

Initially positioned as an AI development tool, Mage evolved into a data infrastructure solution focused specifically on data pipeline orchestration and transformation.^[9] The founders adopted an unconventional go-to-market strategy centered on one-to-one relationships, meeting directly with users through video calls, meetups, and in-person events to build trust in what they describe as critical infrastructure.^[10]

Mage provides data orchestration capabilities designed to simplify the creation and management of data pipelines:^[11]

• Interactive notebook-style editor for building data pipelines
• Support for Python, SQL, and R programming languages in the same pipeline
• Integration with third-party data sources and destinations through pre-built connectors
• Real-time and batch data pipeline processing
• Built-in support for dbt (data build tool) models
• Pipeline scheduling and orchestration with observability features
• Automatic versioning, partitioning, and cataloging of data products
• Visual debugging with instant feedback and real-time monitoring^[12]

The platform treats data as a first-class citizen, incorporating software engineering best practices directly into pipelines, which distinguishes it from general-purpose workflow orchestration tools.^[13]

Mage uses a modular block-based architecture where each stage of a pipeline is a separate file containing reusable, tested code with data validations. This approach differs from traditional DAG (Directed Acyclic Graph) definitions that can lead to complex, interdependent code.^[14] The platform provides Infrastructure as Code (IaC) templates for easy deployment on cloud platforms like AWS.^[15]

Mage has positioned itself as a modern alternative to Apache Airflow, particularly for teams seeking simpler setup and better developer experience.^[16] Industry analyses and comparative reviews frequently position Mage alongside established tools like Prefect and Dagster as part of a newer generation of data orchestration platforms.^[17][18]

The platform has been featured in multiple industry comparisons and tool reviews, with practitioners noting ease of onboarding new team members compared to more complex orchestration tools.^[19] Technology evaluation sites like Airbyte and 5X include Mage in their lists of recommended open-source data orchestration tools.^[20][21]

Mage is available as both an open-source project and a managed cloud service called Mage Pro. The open-source version can be self-hosted on various cloud platforms or on-premises infrastructure.^[22] The platform supports deployment through Docker, Kubernetes, and major cloud providers.^[23]

• Official website
• Mage on GitHub

Category:Data management Category:Open-source software Category:Companies based in Santa Clara, California Category:Software companies of the United States Category:2020 establishments in California