Legends of Mage: User success stories

In the dynamic world of data engineering, efficiency and adaptability are paramount.

, a passionate data engineer at

, found himself facing a unique challenge in handling virtual machines (VMs) provided by customers for exploratory data analysis (EDA) and extract, transform, load (ETL) processes. His journey with Mage transformed this challenge into a seamless experience.

"Mage is really easy to set up locally inside of a VM," Luuk shares, emphasizing the tool's user-friendly nature in addressing their team's specific use case. "Mage helps out a lot with setting it up quickly once we have the 'default' pipelines ready." This streamlined their workflow, enabling them to focus on the core of their work—EDA and ETLing—without being bogged down by cumbersome setup procedures.

"We actually use quite a lot of amazing Mage features!" Luuk's enthusiasm shines through as he elaborates on some standout features:

• Dynamic Blocks

  :

  "Useful for triggering a single pipeline with different runtime vars."

• Conditional Blocks

  :

  "Useful for triggering a chunking pipeline for files that are too big for us to handle in our default pipeline."

• Replica Blocks

  :

  "Including block configs, to slightly change the behavior between the two replica blocks."

"I’m still very pleased with using Mage, and I’ve used both Airflow and Prefect in the past!" Luuk expresses his satisfaction, having navigated other orchestration tools and states "Mage definitely is the easiest to develop in." As Luuk continues to weave his data magic, Mage stands as a silent enabler, ensuring his success story unfolds with each orchestrated pipeline.

Thank you for sharing story, Luuk!

Faced with the intricate task of delivering comprehensive solutions for clients,

, Data Engineer and Founder of

turned to Mage as the go-to ETL tool. The decision was fueled by Mage's seamless deployment, user-friendly interface, and unwavering reliability—qualities that transformed the laborious process into a joyous endeavor.

The standout feature for Chase? Testing at the

.

“The most beneficial feature has to be testing at the block-level. Building a block and then testing it is one of the biggest time savers and easily the best feature of Mage when building a data pipeline.”

Mage's innovative approach allowed Chase to construct individual blocks of the data pipeline and test them in isolation. This not only streamlined the development process but served as a preemptive strike against potential issues. "It's always better to know if you have a leak or a clog before you finish building the house," Chase notes, emphasizing the invaluable time savings and enhanced precision offered by Mage.

In expressing gratitude, Chase commends Mage for its transformative impact on the pipeline-building landscape. The sentiment is rooted in the belief that Mage is not merely a tool but a catalyst for change. As an unwavering advocate, Chase wishes the Mage team continued success, recognizing their contribution to shaping the future of data engineering. With Mage as a trusted ally,

stands as a testament to the tangible difference that cutting-edge tools can make in the hands of a dedicated data professional.

Thank you for sharing your story, Chase!

, BI Engineer Manager at

, a North American integrated marketing agency that builds brands in dimension, had a remarkable success story to share about how Mage transformed his team's ETL pipeline development process and empowered everyone, even those with limited software backgrounds, to create production-grade pipelines.

Thomas' team, consisting of 8 analysts/data scientists and 1 data engineer, initially struggled with building ETL pipelines using tools like Alteryx and Power Query, leading to frustrating results. Determined to improve their workflow, Thomas decided to "ban" Alteryx and PowerQuery for ETL-type pipelines. After a less-than-ideal experience with Airflow, which didn't align with his team's tech and programming skills, Thomas introduced Mage.

Mage proved to be a game-changer. Its user-friendly interface and live pipeline diagram were key features that the team quickly embraced. The dashboard capabilities in Mage were also a hit, allowing analysts to

while building ETL pipelines. The tool's integration of best practices, such as Git and unit testing, streamlined the development process, making Mage the perfect orchestrator for their needs.

What truly stood out for Thomas was how well Mage was thought out and executed. Using Mage felt intuitive, and it seamlessly addressed the pain points his team had experienced with other tools.

"Other than that, there’s a ton of other features that I loved, but overall what ‘sold’ me on Mage is that it feels like everything was though out, and using it just feels right."

Furthermore, Thomas is on the cusp of a significant endeavor. He is leading the establishment of data engineering practices from scratch within his division. The success of implementing Mage for one team has paved the way for its adoption across all other teams, each serving different clients. In the near future, Thomas envisions that dozens of analysts and data scientists within his division will be harnessing the power of Mage, ushering in a new era of efficient data engineering and analytics.

Thomas' vision and adoption of Mage have not only streamlined his team's data engineering processes but also have the potential to revolutionize data practices across his division, setting them on a path to success in the years to come.

Thank you Thomas for sharing your story!

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