Mage

"Deploying Mage was literally the first time I used Terraform and while it was cool to figure out how something works, it pales in comparison with the experience I’m having with Mage Pro... Having Mage Pro has been a real breath of fresh air."?- Rafael Gayoso,?TeachMe.To As an early participant in our private beta, we are excited to highlight Rafael's success with Mage Pro. By utilizing our advanced features and dedicated support, he has effectively addressed his organization's data needs. Mage Pro is built to empower teams of any size to achieve more with their data.??? As we continue our private beta, we're inviting more data engineers to join and elevate their data capabilities. Join our waitlist today: https://lnkd.in/gCcUEP9D

Exciting update from the Mage community! ?? Now you can seamlessly integrate Airtable as a data source in your Mage pipelines. This new feature makes it easier than ever to bring in your data from Airtable and enhance your workflows. Big shoutout to Talaat Hasanin for his awesome contributions! ??♂? ?? Want to see more updates like this or have ideas for future features? Join our growing community of nearly 6500 data professionals to get connected: mage.ai/chat

Hey folks, At Cloud Shuttle, we are looking for some more customers to roll out Mage Pro, their cloud based service to. If you are interested in leveraging an awesome data orchestration tool that is a pleasure to work with, reach out to us to get started. https://www.mage.ai/

?? Unleash the magic of version control within your Mage pipelines! Mage’s GitHub integration is designed to simplify your development workflow, allowing you to effortlessly sync, manage, and collaborate on your projects. Why you should be using Git integration & version control: ???Collaborative Workflow Management: Seamlessly push code to branches on GitHub for streamlined teamwork and code reviews. ? Instant Rollbacks: Mistakes happen. Quickly revert to previous versions and keep your data pipelines running smoothly. ?? Transparent Auditing: Keep a detailed history of every change, ensuring compliance, analysis, and total peace of mind. ???Learn how Mage's version control feature makes collaboration, auditing, and rollbacks a breeze: https://lnkd.in/gZyHV-77 ?? Join the community and start building smarter pipelines today: mage.ai/chat

?? Exciting Project Update: Uber Data Engineering Project with Google Cloud Platform (GCP) ?? I’m thrilled to share that I’ve recently completed a comprehensive project on data engineering and analytics using Google Cloud Platform (GCP) and modern data tools. This project involved analyzing Uber trip data through an end-to-end workflow, leveraging some of the most powerful tools in the cloud computing space. ?? Project Overview: ?? Tools Used: Google Cloud Storage: For raw data storage Mage.ai: For data transformation and pipeline management BigQuery: For scalable data analysis Looker Studio: For insightful data visualization ?? Key Achievements: Data Integration: Seamlessly set up a pipeline to ingest, transform, and load Uber trip data into BigQuery. Data Transformation: Utilized Mage.ai to ensure data was clean and analysis-ready. Data Analysis: Conducted in-depth analysis using BigQuery’s powerful SQL capabilities. Data Visualization: Created dynamic dashboards in Looker Studio to present data insights effectively. In this project, I focused on leveraging Google Cloud Platform’s robust suite of tools, including BigQuery and Google Cloud Storage, to handle and analyze large datasets. Additionally, Mage.ai played a crucial role in streamlining data transformation and pipeline management, enhancing the overall efficiency of the data engineering workflow. Feel free to explore [https://lnkd.in/gUDzvVKM] , contribute, or share your thoughts and suggestions. Reference- Darshil Parmar [https://lnkd.in/gVFPdzfY] #DataEngineering #GoogleCloudPlatform #GCP #BigQuery #MageAI #LookerStudio #Analytics #CloudComputing #MachineLearning #DataScience

Love seeing the magic happen for Salute Loans We’re all about making data fast, simple, and accessible—it’s music to our ears that we’re hitting all the right notes ?? Get started on your magic data journey by joining our community: mage.ai/chat

Discover how Mage AI revolutionizes data pipeline management with its user-friendly interface, dynamic blocks, and seamless integration capabilities. As an open-source tool, Mage AI empowers organizations to streamline data integration and transformation processes, making it accessible for teams of all skill levels. Whether you're a data engineer or a business analyst, Mage AI is designed to enhance collaboration and efficiency in your data workflows. Join the data revolution today! ?? Read the full article to learn more about the features and benefits of Mage AI!

Now that I'm making the rules as the CEO, here's the entire data infrastructure of Salute Loans. There are three managed Postgres databases in Google Cloud that make our medallion architecture. Mage is our data operating system. It's simple, fast, and uncomplicated. When I need to bring in data from a new source and unify it with our existing data lakehouse, it's fast and easy. Mage is sorta like TV on the Radio. You tell people about it, and they're like, "Nah, never heard of them." Then they check out their tunes, and a few weeks later they hit you back like, "Dude, this is absolutely fire." It's like that.

I’m always surprised to not see more Mage around the traps. If you are a data team, give it a shot: https://www.mage.ai

Are you struggling with inflexible data pipelines that can't adapt to changing data volumes or sources? Mage dynamic blocks might be the solution to fix all your problems. Dynamic blocks in Mage are pipeline block add ons that can create multiple downstream blocks at runtime, enabling adaptive and parallel data processing. So what do dynamic blocks have to offer? ? Parallel processing for improved efficiency ? Flexible data handling for complex scenarios ? Scalable workflows without manual intervention ? Adaptive pipeline structures that adjust at runtime One of the best features of Mage's dynamic blocks is their ability to create multiple downstream blocks at runtime. This means your pipeline can: ? Scale automatically based on incoming data ? Process data from multiple sources simultaneously ? Adapt to changing business requirements on the fly Whether you're handling ETL processes, implementing A/B testing, or managing multi-tenant systems, dynamic blocks offer the features to tackle complex data transformation challenges, creating more efficient, scalable data pipelines. How are you currently handling parallel processing in your data pipelines? Have you experimented with any tools similar to Mage's dynamic blocks? Share your experiences in the comments below. If you're getting value from my content, please consider reposting ?? and follow me for more insights on data engineering, Mage AI, and innovative data pipeline solutions. P.S. check out the full article below in the comments. #dataengineering #analyticsengineering #dataanalytics