Our engineers reimagined the limits of what orchestration can do. Today we're proud to announce the new Astro Runtime. Read the announcement blog. Stay tuned for a technical deep dive on how we did it. Reach out today to learn how the new Astro can help your business do more 🚀 astronomer.io/blog/the-new-a…

🚀 Airflow Summit is back, August 31 – September 2 in Austin. Three days, 60+ sessions, and teams from across the industry talking about how they run Airflow in production. As the team behind 100% of Apache Airflow releases and 55% of the code, we'll talk about where the project's headed and what reliability at scale actually takes. Sessions to put on your radar: - Equifax's Yuvaraj Sankaran on running Airflow at the center of data processing across the company - Ramp's Ryan Stevens on orchestrating nearly 100 production ML models with Airflow - Astronomer's Vikram Koka on the state of Airflow: what's shipped since Airflow 3, and what's next We'll be at booth #3 all three days. Book time with the team before you land: astronomer.io/events/airflow…

We've seen Apache Airflow orchestrate business critical workflow. We've also seen it solve a Rubik's cube. Prove what you can build with Airflow 3 in Beyond the Dag, the data engineering hackathon. Go beyond the basic pipeline and pick your track: 🔌 Plugin Powerhouse: Build an Airflow 3.1 plugin that extends the Airflow UI or API 🤖 The Agentic Pipeline: Build a DAG that uses the Common AI provider to orchestrate LLMs and agents 🧑‍💻 Keep a Human in the Loop: Build a workflow with a real human decision point using the HITL operators 🤯 Airflow Can Do That?! The wildcard track. Minecraft, hardware, games, art. Surprise us. Grand prize: Nintendo Switch 2. Category winners get noise-cancelling headphones, a guest spot on the Data Flowcast podcast, and a meet & greet with the Apache Airflow PMC. The hackathon is online, global, and free. The build window is open from now until September 24. We can't wait to see what you build. Sign up now: astronomer.io/events/beyond-…

🤖 Data pipeline needs grow faster than headcount. @togethercompute's answer: AI agents build the pipelines on Astro. Pipeline development is now 20x faster. How it works: An engineer submits a ticket, an agent reads the ticket, authors the Airflow DAGs and dbt models, tests live on Astro via the Airflow MCP, and opens the PR. Humans review. Astro ships it. ⚡ 700+ agent-authored dbt models in production. Deploys in seconds, not minutes.

🆕 New to Airflow? Join @vojaydev for a 60-minute intro to Airflow webinar, where you'll learn: 👩‍🏫 Airflow fundamentals every data engineer needs to know ↔️ How to write and run your first real pipeline 🤖 How teams at @OpenAI, @Uber, and @salesforce use Airflow Register at the link below.

🌁 San Francisco! We can't wait to see you next week at @Snowflake Summit ‘26. Here’s where to find us on-site: 🤖 Visit booth #2605 to check out the latest from Astronomer, including Otto, our new data engineering agent built for Apache Airflow, for your shot at a @KeychronMK keyboard ❄️ Carter Page, @vojaydev, and @StevenHillion each have speaking sessions, covering production AI, context problems, data governance, and unified orchestration ☕ Club @_hex_tech, an elevated lounge for conversations, food, and drinks Link below.

Writing Dags the old-fashioned way is slow, manual, and why your backlog never moves. ✍️ Join Airflow experts for a live walkthrough of modern Dag authoring built for Airflow 3, so you can write better Dags, faster. You'll learn how to: → Generate production-ready Dags faster with agents trained on Airflow best practices → Let analysts define pipelines in YAML within guardrails you set → Use Dag versioning and human-in-the-loop to ship with confidence Link below.

"Building it on Astronomer’s Astro platform means Otto has access to full operational history of an organisation’s data platform, i.e., every pipeline execution, failure, and correction is a record of how data actually moves…and that context is what lets Otto reason about the environment with precision." – @ComputerWeekly @ABridgwater does a deep dive with @JulianLaneve and @tamurphy into all things Otto, our new data engineering agent purpose-built for Apache Airflow. Otto incorporates our team's accumulated Airflow knowledge about what breaks, what works, and what it actually takes to keep a production data platform healthy to make the lives of data engineers easier. Article link below. #ApacheAirflow #Airflow

🆕 Meet Otto: the first agent designed to run your data platform built on Apache Airflow. ⏩ Coding agents are great for writing code. Otto is an Airflow expert, always available directly in your terminal, and built to accelerate any data engineering workflow with: - Data exploration - DAG authoring and debugging - Investigation & root cause analysis - Airflow upgrades - More 🧠 Grounded in the compatibility knowledge and failure patterns from over 1k+ enterprise Airflow deployments, plus your team's specific memory that compounds every session, Otto blends open and proprietary knowledge to help your data team do more. Because there IS a difference between an agent that helps you write pipelines and one that knows your entire Airflow environment, your team's conventions, and what broke last Tuesday at 2 AM. Read more in the blog below and type "astro otto" into your terminal to start using Otto today.

⏩ @cursor_ai. @claudeai Code. MCPs. The way data engineers build locally is changing fast. Apache Airflow experts Tamara Fingerlin and @vojaydev will demonstrate what a modern local dev setup looks like and how to build one yourself. You’ll learn how to: 🌀 Spin up a local Airflow environment using Cursor, Devcontainers, and the Astro CLI ⚡ Write and test Dags faster using the Astronomer Cursor plugin and Claude Code skills 🧠 Apply practical patterns for AI-assisted data engineering that produce suggestions worth using Link below.

Apache Airflow 3.2 is here, bringing partitioned Dag runs and asset events, async Python support for @ task and PythonOperator, and UI theming. This quick notes guide comes with code examples for every new feature to use as patterns in your own Dags. Download the guide to learn how to: ↔️ Pass timestamps between Dags scheduled based on assets without custom workarounds ⚡ Cut task runtime by running concurrent async API calls in a single @ task 🎨 Flag critical production deployments by adjusting the colors in the Airflow UI using an Airflow configuration variable Link below.

🆕 Introducing Blueprint in Astro: self-service Dag authoring for your entire organization. Now in public preview, Blueprint in Astro is built around two distinct roles: 1️⃣ Platform and data engineers define templates using the Blueprint open source framework. 2️⃣ Analysts and other teams build pipelines using those templates and no-code interface in Astro. Once templates are available in your Astro environment, anyone can open Astro IDE and start building. The Blueprint interface presents your organization's approved templates as a library of building blocks, and the Dag gets committed through the same Git workflow as any hand-authored pipeline. This includes the same audit trail and governance. Get started with Blueprint in Astro today at the link below.

🧑‍🚀 NYC, get ready for Spacecamp! You'll hear from data engineering leaders about how they turned hard problems into production-ready systems, before putting your skills to the test in a gamified, hands-on challenge. 🪐 The workshop will focus on AI and LLM orchestration patterns, require basic Python knowledge, and utilize the Astro IDE. Familiarity with Airflow concepts is helpful, but not required. After the workshop, stick around for drinks and snacks on our post-mission Spacewalk. Link below.