Mage

Mage Pro customer success story: Third Space Learning 🎓 Third Space Learning migrated 600+ dbt models into Mage Pro in less than 2 weeks, enabling their team to provide data products supporting 160,000 students and tutors globally. Once the team was onboarded, they quickly moved beyond the constraints they faced with their previous data management service. "Mage has now given us a data orchestration platform that we can very easily use in the business going forward for many tasks” We’re grateful for being able to support their mission, to make high-quality tutoring more accessible so that every student can succeed, by saving their developers’ time spent on infrastructure allowing them to focus on creating value from their data. 🧙♂️ Imagine what your team could achieve with Mage Pro! Express your interest by filling out this short form: https://lnkd.in/gCcUEP9D

🍬 Mage Pro’s pipeline dependency graph turns your project into a real treat! There's nothing scarier than a tangled web of tasks haunting your project's progress. Mage Pro’s pipeline dependency graph creates a visual representation that maps out the relationships and dependencies between different stages or tasks within a workflow, ensuring they execute in the correct order. Gain clear insights into your project's structure with visual mapping, allowing you to easily monitor, adjust, and optimize complex pipelines for smoother execution and reduced errors. Key benefits: 🕸️ Enhanced Visualization: Provides a clear overview of complex workflows, improving understanding and communication among team members. 🦇 Process Optimization: Identifies tasks that can run in parallel and optimizes resource allocation to reduce overall execution time. 🎃 Improved Maintainability: Simplifies the addition, modification, or troubleshooting of tasks by clearly outlining their dependencies. 🌐 Join community with 6700+ data engineers: mage.ai/chat 🔥 Express your interest in Mage Pro by filling out this short form: https://lnkd.in/gCcUEP9D

It’s 7AM, and I’m up early for a spooky Halloween. And what spooky activity am I doing this early morning? Data pipelines! At Archielle Loans, we use Mage as the backbone of our data ecosystem. To use an Alton Brown analogy, the correct knife sharpener is one you use. You’ll use a knife sharpener if it’s easy and doesn’t take a ton of time to get your knives sharp. (If you love cooking as much as I do, you understand the importance of an ergonomic sharp knife.) The same goes for how you store, transform, organize, and manage your data. Mage makes it easy for our team to get the data from where it rests, to where it delivers value. Should you use Mage? Maybe. It all depends on whether you actually like it. There are plenty of data pipeline and ETL tools out there. The most important thing to keep in mind is that no matter what you choose, it should be easy, functional, and fast. And you need to use it. It can’t just sit there. Don’t use dull data. Sharpen it with Mage.

👹 Unmask data inefficiencies with Mage's Global Data Products A Global Data Product elevates the concept of data products—pieces of data generated by components within a data pipeline—by registering them under unique identifiers (UUIDs) and linking them to existing pipelines. By assigning these unique identifiers, you can effortlessly track and audit data usage across all pipelines. This makes these outputs accessible across your entire project, allowing any pipeline to reference and utilize them without the need for regeneration. Why you should implement global data products in your pipelines: 🌐 Universal Accessibility: Eliminate data silos by making data products globally accessible, allowing any pipeline to reference and utilize outputs for seamless collaboration and consistency. 🚀 Optimized Efficiency through Reusability: By reusing existing data outputs, you save time and computational resources, ensuring consistent results across all workflows. ⏳ Smart Resource Management: Data products are generated only when needed—remaining inactive until a pipeline requests them—and configurable settings like "Outdated after" and "Outdated starting at" control data freshness, optimizing resource usage without manual intervention. 📚 Link in the comments to learn how you can implement Global Data Products in your workflows 🌏 Join our open-source community with 6700+ data engineers: mage.ai/chat

We’re hosting a Cloud Shuttle workshop to help startups and lean teams see the full power of Mage and Apache Iceberg. These tools offer both agility and substantial cost savings, making them an ideal choice when you’re working with limited resources but high ambitions. Our founder, Peter Hanssens, will walk you through use cases and practical tips that make implementing these solutions straightforward—and impactful. Save your spot to dive into the details with us: https://lnkd.in/gzDGS4Ak

🤔 Why handling outputs between tasks has to be so complex in Airflow? Xcoms are a learning curve killer when they should work smoothly. Here’s why I think Kestra and Mage have it right: Mage: It lets you pull the output from a previous task directly via python arguments. No decorator black magic. Kestra: Just use {{ output }} and check the output results with jq if you need an specific value. 👉 Give these tools a look. They make task communication feel effortless, and it’s a huge win for building cleaner workflows. #dataengineering #workflowautomation #orchestration

🚀 Exploring the Power of Mage : A Game-Changer in Data Transformation! 🚀 We recently started using mage for our data pipeline needs, and it’s already been amazing! After exploring the options, Mage stood out as the perfect fit for our data pipeline needs. With it's no-code interface and powerful ML capabilities, our team can build, scale, and optimize workflows effortlessly—cutting down on time and complexity. Big shoutout to the Mage team for empowering data teams to build, optimize, and scale with ease! 👏 #DataTransformation #MageAI #Innovation #NoCode

💫 Streamline your data pipelines with sensor blocks to enhance efficiency, reliability, and simplify data management! Smooth and efficient data pipelines are fundamental to successful data engineering projects; however, as pipelines grow in complexity, orchestrating their components can become challenging. Sensor blocks will adapt to your data pipelines to accommodate growing data volumes and evolving business needs, ensuring long-term efficiency and effectiveness. Here's how sensor blocks can transform your data workflows: 📈 Enhanced Reliability: Ensure downstream processes execute only when necessary conditions are met, reducing errors and maintaining data integrity. 💰 Resource Efficiency: Prevent unnecessary executions to optimize computational resources, leading to cost savings and improved performance. ✨ Simplified Pipeline Management: Streamline orchestration by automatically managing dependencies and execution flow. 📚 Link in the comments to learn more on how you can implement sensor blocks in your workflows 🌐 Join our open-source community to learn more about implementing SQL blocks in Mage: mage.ai/chat

I wanted to take a moment to express my gratitude to Tommy Dang and Mage 's team for their incredible leadership and dedication. It's not every day you see a CEO personally follow up to ensure everything is running smoothly. This level of care and attention truly sets Mage apart. I'm thrilled to be part of a platform where such direct and genuine communication exists. Thank you, Tommy, for the outstanding support—Mage is working flawlessly now! #leadership #customerexperience #gratitude #Mage #startupculture #Mageai #BigData #ETL #ELT #DataPipeline #DataScience #AItools #DataOps #data #DataEngineering

Community Spotlight: Jaejun Lee We’d like to spotlight Jajejun for his PR which adds Postgres client package on Mage—giving open source users the power to connect pooling and transaction management. This will save users resources, improve performance, and prevent crashes. Thanks for your contribution and pushing the community forward, Jaejun! Check out Jaejun’s PR: https://lnkd.in/eez_p5Ph 🌎 Join our community of 6700+ data professionals: mage.ai/chat