Astronomer

Day 4️⃣ in our Quickstart #ETL with Apache Airflow 1-minute video series! Next, Tamara Janina Fingerlin shows you how to write a task. 💻 To learn more, get the Apache Airflow Best Practices for ETL and ELT Pipelines eBook: https://bit.ly/3VGSc94

Airflow 3.0 will be the biggest Apache Airflow release in the last five years. I am thrilled to be part of the large community working on this and excited by the progress we are making.

Day 3️⃣ in our Quickstart ETL with Apache Airflow series with Tamara Janina Fingerlin. 📚 Now that you have set up your local Airflow environment and defined your DAG, the next step is to write a task. To learn more, get the Apache Airflow Best Practices for ETL and ELT Pipelines eBook: https://bit.ly/3VGSc94

Day 2️⃣ in our Quickstart ETL with Apache Airflow 1-minute video series! Today, Tamara Janina Fingerlin shows you how to define a DAG. 💻 To learn more, get the Apache Airflow Best Practices for ETL and ELT Pipelines eBook: https://bit.ly/3VGSc94

Julian LaNeve, CTO at Astronomer, is featured in Adrian Bridgwater's Forbes article today about all things modern data orchestration, including the three main layers: 1️⃣ The data layer, where information is stored and managed. 2️⃣ The workflow layer, which defines how data is processed and moved. 3️⃣ The infrastructure layer, which provides the computing resources to run it all. These layers typically run in silos. Read the full article to hear Julian's advice on how to unify these data orchestration layers into a cohesive, full-stack strategy. https://lnkd.in/eNqSbp4K

Robinhood helps your stonks go brrrr with Apache Airflow. 🤑 The company's Airflow architecture powers financial workflow insights at scale, with 15 clusters, more than 4,000 data pipelines, and over $160 billion in assets managed. Learn more details about Robinhood's Airflow architecture, development environments, migration to Airflow 2, and roadmap: https://bit.ly/41Dj5hO

One of multiple Apache Airflow and Astronomer use cases at Ford Motor Company, Mach1ML is the company's multimillion-dollar MLOps platform. Mach1ML relies on Airflow and Astronomer to equip its data scientists and engineers with the tools to efficiently create, deploy, and manage ML models at scale. The platform's architecture includes Airflow and Astronomer for data orchestration, GitHub for version control, and tools like Vertex.AI for model deployment. Airflow also enabled Ford to adopt plug-and-play workflows, ensuring efficient feature engineering, training, and deployment. Learn more about how Astronomer and Airflow help Ford deliver innovation: https://bit.ly/3BwZR2S

"The migration to Astro Cloud resulted in a 40% reduction in maintenance overhead, enabling the team to redirect resources toward innovation. Our decision to move to Astro Cloud wasn’t just about solving immediate challenges - it was about future-proofing our operations and ensuring that we could scale effortlessly while maintaining operational excellence." - Ankush Gautam, Data Engineering and Platform Leader, DataStax Learn how DataStax has doubled down on Astro for its data orchestration and scalability needs, helping them stay ahead in the ⚡-fast world of #GenAI and enterprise data solutions. https://lnkd.in/gqj-jadv

Welcome to our new Quickstart ETL with Apache Airflow video series, presented by Tamara Janina Fingerlin! 📺 Today is the first episode in our series of 9️⃣ , 1-minute videos to help you get started with a simple ETL pipeline. To learn more about best practices for ETL with #Airflow, download the Airflow Best Practices for ETL and ELT Pipelines eBook: https://lnkd.in/gZewDnFK

We've just released Apache Airflow 2.10.4 🎉 📦 PyPI: https://lnkd.in/dK6aFeAA 📚 Docs: https://lnkd.in/d4Awf9pv 🛠 Release Notes: https://lnkd.in/dyY5Nxyf 🐳 Docker Image: "docker pull apache/airflow:2.10.4" Thanks to all the contributors who made this possible.