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

Boa noite rede! Trago hoje um novo projeto! Desta vez, trouxe uma abordagem que une: a plataforma em nuvem Google Cloud Plataform (GCP) e MAGE IA (ferramenta de orquestração), com este conjunto de ferramentas, pude ir desde o armazenamento de dados até a visualização dos mesmos, passando pela etapa de ETL e análise de dados! Confira abaixo os passos que segui, as ferramentas que utilizei e o resultado obtido com este projeto. 📶 Etapas deste projeto: 1º Armazenamento de Dados Brutos: Carregamento de dados brutos para o ambiente do Google Cloud Storage. 2º Modelagem de dados: Nesta etapa, é realizada a análise do uso dos dados obtidos, para este caso os dados serão utilizados para OLAP (Online Analytical Processing), foi selecionado o star schema que é dividido por uma tabela fato (armazenamento de dados quantitativos) e suas dimensões (armazenamento de dados qualitativos) devido ao tamanho do conjunto de dados, não caberia uma modelagem mais complexa como snowflake. 3º ETL (Extract, Transform, Load): O processo de ETL foi realizado em uma máquina virtual no ambiente Google Cloud, utilizando o orquestrador MAGE IA para gerenciar as etapas de extração, transformação e carregamento dos dados. O MAGE IA foi escolhido por sua capacidade de automatizar e otimizar essas etapas, garantindo maior eficiência e precisão no processamento dos dados. 4º Analytics com Big Query: A análise dos dados foi realizada utilizando o BigQuery no ambiente Google Cloud. O BigQuery foi escolhido por sua capacidade de processar grandes volumes de dados de forma rápida e eficiente, graças à sua arquitetura serverless e escalável. 5º Data visualization com Looker Studio: A visualização dos dados foi realizada utilizando o Looker Studio no ambiente Google Cloud. O Looker Studio foi escolhido por sua capacidade de transformar dados complexos em dashboards interativos e relatórios visuais de fácil compreensão. ⚒ Ferramentas / tecnologias utilizadas: Google Cloud Storage: Para armazenamento de dados brutos. Python: Foi útil para realização do processo de ETL. Sql: Linguagem utilizada para manipulação de dados dentro do Big Query. Mage IA: Para orquestração da etapa de ETL. Compute Engine: Computador virtual utilizado para o processo de ETL com o orquestrador Mage IA. Big Query: Para análise dos dados tratados. Looker Studio: Para visualização dos dados de maneira clara e objetiva. Github: Local de armazenamento do projeto. 💡 Resultados Obtidos: O resultado final de um projeto como este é a transformação de dados brutos em insights acionáveis, este projeto passou por todas as etapas da Engenharia de Dados e adentrou no mundo de análise e visualização de dados. Este projeto exemplifica a importância que os dados bem aplicados podem trazer para uma organização, com decisões cada vez mais pautadas em dados, tornando a companhia data-driven. Link do projeto no Github: https://lnkd.in/d3FY_39M

Mage.ai est une solution apparue en 2021, initialement prévue pour être un outil orienté vers le 𝐌𝐋𝐎𝐩𝐬. Mage a depuis pivoté son modèle pour devenir 𝐮𝐧 𝐨𝐫𝐜𝐡𝐞𝐬𝐭𝐫𝐚𝐭𝐞𝐮𝐫 𝐚𝐢𝐧𝐬𝐢 𝐪𝐮’𝐮𝐧 𝐨𝐮𝐭𝐢𝐥 𝐄𝐓𝐋 𝐨𝐩𝐞𝐧 𝐬𝐨𝐮𝐫𝐜𝐞, versatile et aisément déployable 🥰 Notre article décrit la création d'un pipeline qui aura pour but de récupérer, au sein des fichiers Siren, tous les établissements actifs correspondant à un type d’établissement spécifique pour un département. 👉  https://lnkd.in/eCZcVuaU

Are you Struggling to manage to manage your SQL workflows and database schema? Mage SQL Blocks may be the answer to your problems. Mage SQL Blocks are engineered to simplify your data operations, transforming how you work with SQL. Here’s what makes them so special: ✅ Dynamic Data Handling ✅ Automated Schema Management ✅ Advanced SQL Capabilities for Experts ✅ Seamless Connectivity with Other Data Operations One of the most impressive features of Mage SQL Blocks is their dynamic data handling. This feature allows you to easily manage and manipulate large datasets ensuring your data workflows are both efficient and scalable. Additionally, Mage SQL Blocks automate the schema management process, generating the necessary tables automatically based on your specifications. This not only saves time but also reduces the risk of errors. So how do Mage SQL Blocks simplify complex operations? They: ↳ Allow for flexible options to update data without manual intervention. ↳ Enable straightforward data retrieval from multiple sources in one go. ↳ Support batch processing of SQL commands to optimize performance. With these powerful tools at your disposal, data engineers can focus on what really matters: delivering solutions that have a positive impact on the business. Want more on using Mage SQL blocks, check out the full article below in the comments. How do you tackle SQL in your data workflows? Share your experiences in the comments below 👇 If you are getting value from my content please consider reposting ♻ and follow me for content on SQL, Mage, and Power BI. #sql #dataengineering #dataanalytics #analyticsengineering

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.)