My Experience With Mage
Why I chose Mage to improve our data pipeline development process and experience.
Introduction
Earlier this year, I stepped into a new role as a Data Engineer; I was excited to take a step back from management and focus on solving technical problems. I was tasked with enhancing while simplifying our data pipeline process. The goal was to make the pipelines more maintainable, straightforward to change, and ensure greater reliability and observability. I researched several tools. 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.
The Search for a Solution
I considered several established industry standards in my hunt for a suitable tool. However, what set Mage apart was its focus on enhancing the data engineer's development experience. I wanted this to be something non-engineering teams could use and self-serve their data transformation and integration needs. As someone aiming for fast and effective solutions with solid support, Mage emerged as the frontrunner.
First Impressions and Learning Curve
The Quickstart and Get Started sections in Mage's docs were incredibly easy to follow. Seeing how swiftly I could begin developing pipeline code was encouraging, and the ease of building a pipeline was beyond my initial expectations. Mage's intuitive interface and well-structured tutorials facilitated a smooth learning curve, significantly reducing the development time. I've spent time teaching others how to use Mage, and it's been fun to see how quickly people take to it.
Practical Benefits and Features
Mage’s impact on our data pipeline process was nearly immediate. We replaced around 20 disparate Python scripts, which were challenging to maintain, monitor, and orchestrate. Using Mage, we replaced most of these scripts within a week and introduced essential features like observability, monitoring, alerting, SLA tracking, and data tests. The transition to Mage marked a substantial upgrade in our pipeline management and development process.
Mage’s Core Abstractions
One of the standout features of Mage is its core abstractions], which include Blocks, Pipelines (DAGs consisting of blocks), and Triggers (how a pipeline is initiated). These abstractions encouraged and enabled the creation of simple, reusable code blocks, making even the most complex pipelines easy to parse and manage.
More on blocks. Blocks are the fundamental building blocks for Mage pipelines and are pretty cool. You have many different types of blocks that help you keep your project clean and easy to navigate. There are many different block types. The basics include Loaders, Transformers, and Exporters. These are the standard ELT/ETL blocks. They included more advanced blocks such as Conditional, Sensor, and dbt blocks. Dbt blocks warrant their own article, but you should check them out if you use or are interested in using dbt.
Hosting and Community Support
Deploying Mage in a production environment was a breeze, thanks to Mage's comprehensive deployment documentation, terraform templates, and an incredibly responsive community. Whenever I encountered a hurdle or needed advice for scaling, the Mage team on Slack was always quick to respond and helpful. This was critical since it was so new; I wanted to ensure we could get the help we needed and that the repo had a lot of activity. Fun fact: they have averaged 1.21 weekly releases since I started using Mage. More than a few of those releases included features and requests I made a day or two prior. I'm often blown away by the team's responses to feature requests in their Slack. Usually, it's something like, "Great idea! We'll add it to the roadmap." If it's a blocking feature, you will usually see that change in the next release. They will also ping everyone who requested it once it's live.
Conclusion
Mage has been a game-changer in our data pipeline management. Since its adoption, the improvements in efficiency, reliability, and data quality are a testament to its capabilities. Mage is not just a tool; it's an enabler for data engineers seeking to revolutionize their pipeline processes. You can have fast iteration and security while maintaining high data quality.
For those dealing with untangling spaghetti data pipelines, I highly recommend exploring Mage. Whether you are looking to simplify your process, enhance features, or seek reliable support, Mage stands out as a strong option in data engineering. If you have any questions about Mage or want to know more about my experience with it, reach out! Also, stay tuned for more Mage and Data Engineering content.
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