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

Week 5 of LLM-Zoomcamp coming to a close quick. 🏃♂️🏁 This Week was ORCHESTRATION!!!!! (you have to say that part in a loud kingly kind of voice) 😉 Specifically we used with Mage. Going in I thought it was going to be a LOT of functions, copy and paste from the repo, tracking down obscure errors and was bracing myself for a hard grind. And then.... Tommy Dang busted out with 8 YouTube Videos all under 2 minutes and 30 seconds AND provided setup files so when we went into Mage it was a quick click, click BOOM to get it done! Even had a Retrieval Augmented Generation option when you create a new pipeline!!! 🤯 I've used Mage in the past and still do from time to time. Why? I love the look and feel of the navigation. It's one of those things that's hard to categorize, but the smooth navigation, adding blocks and sweet visuals when building pipelines makes it feel "cutting-edge" and makes me feel cool. And I WANNA BE COOL! 😎 🙂 And while I can't say using Mage will make you cool, I think you'll look cool building pipelines to anyone looking over your shoulder while you use it. 😅 BUT, you're already cool and you know it. 😉 🤗

FREE Data Engineering Fundamentals + 6 End-To-End Projects 📈 Kick-start your career in Data Engineering with these projects, you will learn more than any paid courses for FREE! Spend your weekend doing these amazing projects 👇🏻 1. Start with watching Fundamentals of Data Engineering 3 Hour Video https://lnkd.in/dAhXyb2G You will learn: - What is Data Engineering? - Data Engineering Lifecycle - Data Generation & Storage - DBMS System - Data Modelling - NoSQL Databases - SQL vs NoSQL - Data Storage Processing - OLAP vs OLTP - Extract Transform Load - Data Undercurrents - Data Architecture Complete Guide - Data Warehouse - Dimension Modelling - Slowly Changing Dimensions - Data Marts - Data Lake - Data Lake vs Data Warehouse - Big Data Landscape - Data Engineering on Cloud - AWS Data Services - Real-World Case Study Architecture on AWS - GCP Data Services - Real-World Case Study Architecture on GCP - Azure Data Services and many more... ====FREE Projects==== 1. IPL Data Analysis (End-To-End Apache Spark Databricks Project)- https://lnkd.in/dQiMq6PJ What will you learn? ✅ Python and PySpark ✅ SQL ✅ Apache Spark Basics and Databricks ✅ Writing transformation logic ✅ Visualizing data for insights 2. YouTube Data Analysis (End-To-End Data Engineering Project) - https://lnkd.in/d5BRZfXv What will you learn? ✅ Python and PySpark ✅ SQL ✅ How to understand the business problem ✅ AWS Services - Athena, Glue, Redshift, S3, IAM ✅ Building Data Pipeline and Scheduling it 3. Twitter Data Pipeline using Airflow - https://lnkd.in/dE2VvdSg What will you learn? ✅ Python ✅ Basics of Airflow ✅ Working with Twitter Data and Package - Tweepy ✅ Python Package - Pandas ✅ Writing ETL job and storing data on S3 4. Stock Market Real-Time Data Analysis using Kafka, AWS, and Python - https://lnkd.in/dah8Au3B What will you learn? ✅ Build a Real-Time app using Python ✅ Understand the basics of Kafka ✅ Install Kafka on EC2 ✅ Generate a real-time pipeline and ✅ Analyze Data in Real-Time 5. Uber Data Analytics Project On GCP Video Link - https://lnkd.in/daiFAMHT Here's what you will learn: ✅ How to understand raw data ✅ Building Data Model (Lucid Chart) ✅ Writing ETL Script (Python) ✅ Modern Data Pipeline Tool (mage) ✅ SQL queries for analysis 6. Olympic Data Analytics | End-To-End Azure Data Engineering Project Video Link - https://lnkd.in/dEtjqhar Here's what you will learn: ✅ Extract Data from APIs ✅ Learn Azure Services DataBricks, DataFactory, and Synapse Analytics ✅ Writing Spark Code ✅ SQL queries for analysis Tag someone who might find this helpful 👇🏻 Have you ever done any of these projects? Let me know!

Want to save huge 💰💰💰 on your data platform… take Cloud Shuttle’s 5 day challenge to get your analytics setup looking like a Ferrari than a Festiva! In those 5 days we will set you up with: 1. A Mage cluster (pro or self managed) 2. An Iceberg data lake 3. An automated data pipeline to load in data into Iceberg Bonus points: 1. Real time data feed using Kinesis and ClickHouse 2. Streamlit dashboard to surface up insights in realtime Reach out if you’d like to take us up on our 5 day data platform challenge to save you big on your data platform!

As a single-person data engineering team 🚀 Before Mage: I had to dive deep into every aspect of the data pipelines and infrastructure to ensure stability. Despite having monitoring and self-healing mechanisms, the constant attention to detail still left me feeling stressed and anxious. After Mage: Everything changed. Now, I can just sit back, relax, and let Mage AI handle all the hard work. It gives me accurate and reliable results without any stress. It’s really made my work so much easier and brought me peace of mind. I'm truly grateful to the Mage team for bringing this ease and peace of mind into my work. 🙏 join slack: https://lnkd.in/d_F_nWpw  #Automation #MageAI #ETL #ELT #BigQuery #GoogleCloud #GCS #PostgreSQL #MongoDB #Python #Kafka #Qdrant #Elasticsearch #DataEngineering #DataPipeline #NoSQL #DataManagement #DataIntegration #Automation

🌟 Community Spotlight of the week: Ivan Barbosa Pinheiro 🌟 Despite being new to the community, Ivan has already made an impact by sharing an outstanding tutorial on getting started with Mage. His guide is an invaluable resource for anyone looking to kickstart their journey with Mage 🧙♂️Check out his post and explore the full tutorial in his GitHub repository. Thank you, Ivan, for your contribution and for helping spread the Magic. 👉 Link to Ivan's post: https://lnkd.in/epsWUbAE ✨ Join the community: mage.ai/chat

I was blown away when Tommy Dang showed me this demo back like... six months ago. Mage Pro is one of those tools that can take an existing data engineer, and make them capable of 10x... or heck maybe even 50x the work? It's insane how good this product is for building data pipelines. (Full disclosure: I don't get paid to say this. Tommy is just a homey and Mage is an insanely good product.)

🚀 Effortlessly migrate your data projects to Mage Pro, our managed service. We've designed our onboarding process to make migrating your existing pipelines to Mage Pro seamless. With our user-friendly onboarding interface, your team can immediately start experiencing the full potential of your data. Unlock powerful features like LLM capabilities and Kubernetes job configuration, all while cutting your infrastructure costs by up to 50%. 🧙♂️ Imagine what you could achieve with Mage Pro! Be among the first to unlock exclusive access to our private beta: https://lnkd.in/gCcUEP9D

"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

ETL com Ferramentas de Dados Modernas Tenho estudado algumas ferramentas que frequentemente aparecem em posts sobre Modern Data Stack aqui no LinkedIn. Recentemente, me deparei com um post no blog do Simo Ahava que foi compartilhado nessa rede (link abaixo) sobre uma ferramenta chamada Mage, o que despertou minha curiosidade para explorá-la mais a fundo. Ao visitar a página oficial do Mage, fiquei impressionado com a proposta da ferramenta como uma alternativa ao Airflow, mas com uma interface mais amigável e com a possibilidade de utilizar blocos de código em Python semelhantes aos do Databricks (inclusive, é possível utilizar o Spark!). A apresentação do produto me convenceu a experimentá-lo, e foi exatamente o que fiz. Após criar vários ambientes e até implementá-la em produção no GCP para avaliar o processo de deploy, decidi elaborar um pequeno tutorial usando o Mage.IA como orquestrador de uma pipeline simples de dados de marketing do Meta Ads, com as tarefas de extração gerenciadas pelo Airbyte. Abaixo, compartilho o desenho da arquitetura que idealizei para esse caso. O link do repositório que contém um tutorial mais completo para testar o ambiente está nos comentários. Considere testar o projeto e deixe sua sugestão e avalição! Post do blog Simo Ahava (https://lnkd.in/dYGbU7Rc)