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.
This guide covers best practices for everything from choosing the right Airflow deployment model to configuring your DAGs for optimal performance.
The new provider will make it easier for organizations to use Airflow to automate and manage their Fivetran pipelines.
We are excited to announce the partnership between Astronomer and Snowflake, aimed at revolutionizing the way organizations leverage data and execute their data pipelines.
Use Apache Airflow with DuckDB and MotherDuck in three different ways. Access the DuckDB Python package directly, leverage the DuckDB Airflow provider, and use DuckDB with the Astro Python SDK.
Use ~1,000 open-source Airflow operators and define your own custom operators in the Astro Cloud IDE with the newly released cell type functionality
Here’s a look at seven alternatives to MWAA.
Picking the right tools for your data stack depends on your exact business and engineering needs, and the choice may seem daunting. Thankfully, there are several popular tools, each with thousands of users, all with a unique approach for managing data pipelines.
Migrating the Astronomer Registry’s backend from Airtable to Postgres and a Golang REST API
See how Astro can help you stay ahead of Data SLAs and commitments to stakeholders
Try Astro free for 14 days and power your next big data project.