Exploring the power of Mage: Insights from the data engineering community

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"This project is designed to build an end-to-end data pipeline that generates a data mart for sales reporting using Mage.ai. The goal is to provide daily, weekly, and year-to-date (YTD) sales insights, including critical business metrics such as gross revenue, discounts, cost of goods sold (COGS), and net profit."

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"In this project, I was able to combine my love for sports and my passion for all things data into one amazing data engineering project. Above is the overall structure of what I built."

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"This article describes the creation of a pipeline that will aim to retrieve, within the Siren files, all the active establishments corresponding to a specific type of establishment for a department."

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, Data Scientist

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In this tutorial, we will explore five alternatives to Airflow that offer enhanced capabilities and address some of its limitations. Moreover, we will learn to build a simple ETL pipeline using each tool, run it, and visualize it in their dashboard.

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, Principal Consultant DevOps

"In this article, the focus is on MageAI, an open-source data pipeline tool designed for efficient data integration and transformation. It outlines MageAI’s features, advantages, and provides step-by-step instructions for setting up MageAI using Docker, Kubernetes, Helm charts, and managing projects with Git. The article serves as a comprehensive guide for data professionals interested in exploring and implementing MageAI for their projects."

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, Freelancer | BI & Finance Reporting

"Mage.ai is one of the solid alternatives to Apache Airflow. It is a lighter and, from my perspective, much more user-friendly option for working with ETL data flows. It includes a wide range of predefined connectors (Python, SQL), so you don’t have to write code from scratch. Development is thus time-efficient. However, Mage can do much more."

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, 4X Top Writer | Founder/CEO @ Everything Programming

"In this blog, I will review how to deploy a Mage Docker container with Amazon Elastic Container Service (ECS) on AWS Fargate and Amazon Elastic Container Registry (ECR)."

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, Data Consultant

"In this blog, I will review how to deploy a Mage Docker container with Amazon Elastic Container Service (ECS) on AWS Fargate and Amazon Elastic Container Registry (ECR)."

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, Digital Data Engineer at J.R. Simplot Company

"We conclude the data flow project with the step of developing a business intelligence dashboard to present machine data, at a reliable schedule, to a leadership or maintenance team."

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, Data Engineer @ Odicis | Freelancer

"In the rapidly evolving landscape of data engineering and data science, tools like Apache Airflow and Mage.ai have emerged as significant players. Each offers unique capabilities in managing, orchestrating, and automating data workflows. This article delves into a detailed comparison and description of Airflow and Mage.ai, illuminating their features, strengths, and ideal use cases."

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, Digital Data Engineer at J.R. Simplot Company

"We begin to wrap up our data flow project with an introduction to visual-based (GUI) tools and dashboards to present the data ingested from the Google Sheets fault data source."

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, Data Engineer at

"Mage AI isn't just another tool; it's a solution to the evolving demands of data automation. This blog aims to explain how Mage AI is changing the way we automate data pipelines by addressing challenges and introducing innovative features. Let's explore this evolution, understand the problems we face, and see why we’ve adopted Mage AI."

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, Digital Analyts at 

"Mage is a data engineering tool that allows you to build your ETL (extract, transform, and load) pipelines. What I love about Mage is that it is easy to use, you can visualize your data pipelines and it supports multiple programming languages: SQL, Python.. and R!"

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, Digital Data Engineer at J.R. Simplot Company

"So essentially, we’d like to have a single tool that combines the functionalities of a data orchestrator and a tool for data transformations, closely integrated. Moreover, we desire it to be devoid of over-engineering, allowing us to quickly design new pipelines and incorporate custom Python code without much fiddling."

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, Digital Data Engineer at J.R. Simplot Company

"In this article, we will explore the inbound integration to the mage.ai pipeline, connecting to the spreadsheet data source using the Google Sheets API through a JSON credentials file."

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, Digital Data Engineer at J.R. Simplot Company

"Data pipelines are software services that progress the data from source to storage, hopefully without too much programming complexity. In this article, we begin by initializing the mage.ai software."

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, Digital Data Engineer at J.R. Simplot Company

"Alas, we must now, with great reluctance, enter the world of data engineering. You need a way of automating batch data exports from the machine using long-term storage space to store this data. A dynamically updated report as live data is refreshed would be nice as well."

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, Senior Data Engineer at Nursa

"I wanted something open-source that we could host ourselves. I also wanted something that people on the team who may not have as strong of a SWE background could easily use. I came across Mage; it checked all of the boxes I had. Mage has since transformed our pipelines and my perspective on data engineering."

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, Data Engineer at LoopStudio

"Mage tends to explode the benefits of using a orchestration such as the template creation and the use of several programming languages. You can create pipelines in python, R and sql."

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, Consultant at Slalom

"Mage AI is an open-source data pipeline tool that brands itself as the 'modern replacement for Airflow'. I found Mage to be an intuitive and effective tool for this project, largely because it made it easy to start writing the code I wanted to with pre-made templates..."

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 , Developer Advocate at InfluxData

"Mage is an open source data pipeline tool for transforming and integrating data. You can think of it as a replacement for Airflow. In this tutorial we’ll scrape the surface of using Mage.ai to build materialized views of our time series data in InfluxDB Cloud v3."

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, Vice President - Data at Morgan Stanley

"Mage comes with notebook type UI which helps you build your pipelines and provides pipeline blocks(below) which are individual files which are reusable. Same block can be reused by multiple pipelines within a given project."

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, Innovative Tech Mind | Data Engineering | Data Enthusiast

"The dynamic realm of data engineering requires a trusty companion in the form of a versatile and user-friendly pipeline tool. Meet MAGE, the ultimate guide in the intricate world of data transformation and management. In this article, we will embark on an exploration of MAGE’s remarkable attributes, perfectly tailored for novices, to simplify the enigmatic realm of data engineering."

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, Data scientist/ML Engineer at HP Analytics

"In a data-driven world, harnessing the power of ETL pipelines is crucial. With the user-friendly approach of Mage.AI and the robust database management capabilities of MySQL Workbench, constructing an ETL pipeline becomes a breeze."

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, Data Engineer at Healthplus.ai

"In Part 1 you’ve learned what Mage is, how to build a data pipeline using Mage, and what different types of blocks there are.

In Part 2, you’ll learn more about how to boost your pipeline quality through the add-ons that you can apply on all blocks."

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, Data Engineer at Healthplus.ai

"One of Mage’s main differences with the other orchestration tools is that it has a note-book style web UI, that you can develop your code in as well as connect up-/downstream dependencies. This makes the data pipeline journey extremely visual, something that is normally just a piece of code now has visual aspects to it."

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, Senior Data Engineer at Dott | Medium Writer

"Mage addresses a few pain points of Airflow such as local testing and data pass between tasks. It also has a really intuitive UI that helps engineers build data pipelines within a matter of minutes."

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, Senior Data Engineer at Dott | Medium Writer

"Overall, Mage simplifies the development process and provides a convenient way to work with your data."

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, Senior Data Engineer at Dott | Medium Writer

"Mage is one of the growing data pipeline tools that is seen as a modern replacement for Airflow. Its four design concepts set Mage apart from Airflow and we can sense the difference right from the beginning of the development cycle."

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, Data engineer sharing insights and best practices on data pipelines, ETL, and data modeling

"The combination of Terraform, AWS Lambda and S3, Snowflake, DBT, Mage AI, and Dash provides a robust and flexible pipeline that can handle large volumes of data and deliver valuable insights."

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"In this article, I will talk about the features and functionalities of Mage, highlighting what I have learned so far and the first pipeline I’ve built using it."

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, MLOps Engineer at Ocelot Consulting

"Mage is the ideal tool to complement your dbt project if you seek improved efficiency and are open to a slight increase in project complexity."

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, Senior Solutions Engineer at Infoworks.io

"In this article let us see how we can use Mage and perform ETL (extract, transform, load) to transfer data from Freshdesk, a customer support software, to Snowflake, a cloud-based data warehousing platform. The goal of this project is to streamline the process of transferring data from Freshdesk to Snowflake, enabling faster and more efficient data analysis."

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"Mage is a data pipeline tool designed to make data engineering tasks more accessible and efficient. In this tutorial, we will walk you through building a standard batch pipeline using Mage, integrating it with DBT, and exploring data integration pipelines."

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, Python Developer & Technical Writer

"Mage.Ai is a cloud-based dataflow orchestration tool that allows users to easily build, schedule, and monitor data pipelines. It provides a drag-and-drop interface for building workflows and supports a wide range of data processing language such python, SQL, R."

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, Data Engineer passionate for automating tasks, Big Data and cutting-edge Technologies

"Although I have to admit that implementing Airflow with Astronomer greatly improved the UI, deployment locally, and the ease with which we data engineers use Airflow today,

is something more because it doesn’t just allow us to orchestrate and connect sources and destinations with a couple of clicks while allowing us to view at the same time data and our blocks (Tasks), for this reason, I encourage all Data Engineers colleagues around the world to test the following phrase in the image."

Written by

, Long time data engineer, with a passion

"Instead of Mage just being another ETL and Orchestration tool with a slightly different take from Airflow (Prefect), Mage tries to fundamentally change the way data pipelines are developed and used, focusing on the developer and Engineering aspects to set itself apart."

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, Data Engineer at Socure

"Mage is all in one tool that enables you to perform ETL, integrate multiple data sources, orchestrate and monitor pipelines with built in native notebooks to update code on the fly in an intuitive user interface."

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, Data Engineer at IKEA

"My favourite aspect of Mage is the web-based IDE that is super intuitive. The UI definitely catches the eye or as I’ve heard in drag slang “she’s serving face”. YES Mage."

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, Head of Data at 

"However, with the help of powerful data pipeline tools like Mage.ai, businesses can efficiently manage their data processes and synchronize data between different databases, such as Couchbase and BigQuery."

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, Data Engineer at Apple

"A nice thing about Mage is that each task is executable. Without triggering the entire pipeline, we can perform more testing for each task before we build the entire DAG."

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, Data Engineer at Apple

"The mage-ai UI is more interactive than the Airflow UI. In Airflow, you cannot change the dependencies directly on the Airflow webserver. To modify the dependencies, you’d need to write and define them in the code."

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, Data Platform Lead at ZebPay

"Mage is bundled with a rich UI with an end to end functionality control experience when it comes to data pipeline. Starting from development of a pipeline till the launch of the pipeline in the production, do your entire workflow through Mage with the integration of version control tools."

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, Long time data engineer, with a passion

"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."

Written by

, Senior Data Engineer at Miniclip

"Mage is much more than what we have discussed in this blog. And we wouldnt be able to cover all the cool features."