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

Written by

, 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."

Written by

"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."

Written by

, 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."

Written by

, 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."

Written by

, 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."

Written by

, 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."

Written by

, 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."

Written by

, 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."

Written by

, 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."

Written by

, 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."

Written by

, 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."