Micropipelines: A microservice approach for DAG authoring in Apache Airflow

We have wanted to add Data awareness to Airflow for a long time and it's finally here!

With Airflow 2.4, you can now break down big, monolithic pipelines—in which long-running tasks can delay the completion of time-critical ones—into smaller, loosely coupled “micropipelines” that can be orchestrated together. Conceptually, these are microservices for data, a transformative development made possible by the new release’s introduction of Data Driven Scheduling and a new Airflow abstraction called Datasets.?

I am excited by the capabilities that micropipelines enable such as:

• Helping teams tune their entire data ecosystem to balance time criticality vs. cost using
• Enable collaboration between data team members and make it easier for them to use the right language—e.g., Python or SQL—for the job.

I am also thrilled by the easier authoring process for data pipelines aka DAGs enabled by the AstroSDK. This open source DAG authoring framework building on Airflow is amazing!

Kudos to all the people who have worked on this across both Airflow and AstroSDK: Andrey Anshin, Ankit Chaurasia , Ash Berlin-Taylor , Bartlomiej Hirsz, Brent Bovenzi , Daniel Imberman , Daniel Standish , Drew Hubl, Elad Kalif , Ephraim Ewele Anierobi , Ernest Rohit katta , Jarek Potiuk , Jedidiah Cunningham , Josh Fell , Kaxil Naik , Mark Norman Francis, Matt Rixman , Mike Shwe , Niko, Pankaj Koti , Pankaj Singh , Phani Kumar V , Rahul Vats , Tatiana Al-Chueyr Martins , Tzu-ping Chung , Vincent, Woojciech Januszek, Chethanuk-plutoflume, PierreJeambrun and everyone else (all 152) who committed! Thank you all for making Airflow the most popular Apache project (measured by contributors) ever!

#apacheairflow #opensource #dataengineering #airflow