Introducing Apache Airflow 2.9
- Kenten Danas Manager,
Developer Relations
The Airflow 2.9 release brings significant enhancements to user-favorite features like data-aware scheduling, dynamic task mapping, and object storage.
The Airflow 2.9 release brings significant enhancements to user-favorite features like data-aware scheduling, dynamic task mapping, and object storage.
An introduction to testing strategies, best practices, and implementation techniques.
Our beta cohort of 10 is now joined by 23 hand-selected individuals who, we believe, truly embody what it means to champion the Apache Airflow Project.
Apache Airflow is at the core of many teams’ ML operations, and with new integrations for Large Language Models (LLMs), Airflow enables these teams to build production-quality applications with the latest advances in ML and AI.
Introducing Apache Airflow™ on Astro, an Azure Native ISV Service. This partnership with Microsoft seamlessly embeds Apache Airflow into the Azure ecosystem, offering a unified environment for scalable, secure, and easy-to-manage mission-critical data pipelines.
Explore the TaskFlow API and traditional operators and find out how to combine them for dynamic, efficient DAGs.
See how the new Tecton Airflow Provider can make your feature pipeline orchestration within Apache Airflow more efficient.
In part 3 of Ask Astro series, uncover key considerations for data ingestion in Retrieval Augmented Generation with LLMs. Find out how to select the ideal vector store, model, schema design, and chunking strategy for your project.
The state of deploying pipelines with dbt has changed considerably in the last few months. Over the last few weeks, I was working with Astronomer to test out their new tool, Cosmos, to deploy dbt workflows onto Snowflake.
Part 2 of our blog series offers an in-depth comparison between Databricks and Airflow from a management perspective. Explore the differences in setup, monitoring, integrations, scalability, and customization.
An example project showing how to use Apache Airflow to orchestrate a machine learning pipeline with the Snowpark provider and Snowpark ML.
Learn how easy it is to migrate your Python scripts into Directed Acyclic Graphs (DAGs) with modular tasks, orchestrate them in Apache Airflow, and use Airflow features to enhance the efficiency of your jobs.
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