Mage

🚨 Oh we're just getting started, bub! Deadpool & Wolverine release v0.9.73 🥷🐺 🧠 Memory management upgrade 🔄 Dynamic blocks 2.0 🔐 Microsoft Azure DB connection via Key Vault 👁️ Workspace monitoring 🐛 Bug fixes & more! Details: https://lnkd.in/gDrqm2Ds #dynamicblocks #azuredb #keyvault #changelog #dataengineering #mage #mageai 

🔥 Community Spotlight of the week: Cristopher De La Cruz 🔥 Cristopher contributed directly to Mage's open-source community by creating a feature that adds trim functionality to the reformat action in a python transformer block—helping with data cleaning and data preparation. Thanks for your contributions, Cristopher! Keep spreading the magic. 🔮 Get connected, and join our Slack community: mage.ai/chat

🔥 Big news for data engineers: RAG is coming to our managed service, Mage Pro—now in private beta! 🔥 Retrieval-Augmented Generation (RAG) is a game-changer for data engineers looking to supercharge their data workflows. Here’s how: 🚀 Accelerated Insights: With RAG, you can generate more accurate insights faster than ever. This means less time waiting for results and more time acting on them. 🔮 Enhanced Data Retrieval: RAG leverages advanced AI to retrieve the most relevant data from vast datasets, ensuring you always have the information you need at your fingertips. 🐲 Seamless Integration: Our RAG feature integrates effortlessly with your existing data pipelines, providing a smooth and efficient user experience. To get exclusive early access to RAG, and a ton of other advanced features, express your interest in Mage Pro by filling out our interest form: https://lnkd.in/gCcUEP9D

Mage.ai: First Impressions of a Powerful Data Pipeline Tool I've recently explored Mage.ai and found it to be an impressive data pipeline tool. What stands out is its real-time visualization feature, allowing users to see the output at each step of the data transformation process. This immediate feedback loop significantly enhances the development experience and troubleshooting efficiency. Mage.ai's intuitive interface and powerful capabilities make it a valuable asset for data professionals looking to streamline their ETL workflows. #DataEngineering #ETL #DataPipelines #Mageai #DataTransformation #BigData #DataScience#Analytics #TechTools #DataVisualization #BusinessIntelligence #DataInfrastructure #CloudComputing

Excited to share my recent experience with Mage, a cutting-edge ETL tool that's transforming the way we handle data workflows! Mage has been a game-changer in simplifying and automating data extraction, transformation, and loading processes. Its intuitive interface and powerful features have made it easier for me to build and manage data pipelines efficiently. Mage's flexibility and scalability make it an excellent choice for optimizing data operations. It's been incredible to see how much more efficient data handling can be with this tool. If you're studying or working in data engineering or data science, I highly recommend giving Mage a try. Let's connect and share our experiences with this fantastic tool! https://www.mage.ai/ #DataEngineering #ETL #DataIntegration #MageETL #DataScience

We're starting module 5 of the LLM Zoomcamp where we'll practice LLM orchestration and ingestion. We'll cover: 🔸 Data ingestion and chunking 🔸 Tokenization 🔸 Data embedding and exporting to the elasticsearch 🔸 Retrieval: testing vector search query 🔸 Schedule and trigger daily runs Thanks, Mage and Tommy Dang, for preparing this module's content! Start learning now: https://lnkd.in/eKfb44Pg

🌠 Community Spotlight: Ashkan Golehpour 🌠 Recently migrating from Airflow to Mage, Ashkan has become a Mage superuser, transitioning 23 pipelines over which have completed nearly 10k runs since their initial migration. Additionally, he’s taken full adage of Mage’s capabilities, applying Mage to a variety of use cases: ETL processing, BigQuery operations, streaming, and business integrations. 🚀 Thanks for being a superstar user of Mage and sharing your data journey with us, Ashkan! 🧙♂️⭐ Join out Slack community: mage.ai/chat

Take your data operations to gold with our new RAG pipeline feature, available exclusively on Mage Pro private beta 🥇 RAG (Retrieval Augmented Generation) is a machine learning model that combines information retrieval with text generation to provide accurate and contextually relevant responses. By integrating RAG models into workflows, data engineers can significantly enhance the efficiency, scalability, and effectiveness of their data operations, leading to more robust and intelligent data solutions. 🧙♂️⭐ Interested in getting access to RAG? Sign up for our private beta to be one of the first to experience this new feature! Link in the comments 👇

Are you struggling to organize your data workflows efficiently from ingestion to analytics-ready datasets? Let's break down how Medallion Architecture can revolutionize your data pipeline: ✅ Bronze Layer: Raw data landing zone ✅ Silver Layer: Cleansed and standardized data ✅ Gold Layer: Analytics-ready, optimized datasets This tutorial walks you through implementing Medallion Architecture using Mage, here’s what you’ll learn ↳ How to set up Mage ↳ Configuring your project ↳ Generating sample medical data ↳ Building each layer using SQL transformations ↳ Progressing from raw data to analytics-ready datasets You can do this all this within Mage no integrations necessary. Check out the full article linked in the comments below. 👇 If you are getting value from my content please consider reposting ♻ and follow me for content on SQL, dbt, and Power BI. #dataengineering #analyticsengineering #dataanalytics #sql

Check out, Ashkan Golehpour's epic post on how he updated his migration from Airflow to Mage! 🔥🧙♂️