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The Data Flowcast에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 The Data Flowcast 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.
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Inside the Custom Framework for Managing Airflow Code at Wix with Gil Reich

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The Data Flowcast에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 The Data Flowcast 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.

Efficient orchestration and maintainability are crucial for data engineering at scale. Gil Reich, Data Developer for Data Science at Wix, shares how his team reduced code duplication, standardized pipelines, and improved Airflow task orchestration using a Python-based framework built within the data science team.

In this episode, Gil explains how this internal framework simplifies DAG creation, improves documentation accuracy, and enables consistent task generation for machine learning pipelines. He also shares lessons from complex DAG optimization and maintaining testable code.

Key Takeaways:

(03:23) Code duplication creates long-term problems.

(08:16) Frameworks bring order to complex pipelines.

(09:41) Shared functions cut down repetitive code.

(17:18) Auto-generated docs stay accurate by design.

(22:40) On-demand DAGs support real-time workflows.

(25:08) Task-level sensors improve run efficiency.

(27:40) Combine local runs with automated tests.

(30:09) Clean code helps teams scale faster.

Resources Mentioned:

Gil Reich

https://www.linkedin.com/in/gilreich/

Wix | LinkedIn

https://www.linkedin.com/company/wix-com/

Wix | Website

https://www.wix.com/

DS DAG Framework

https://airflowsummit.org/slides/2024/92-refactoring-dags.pdf

Apache Airflow

https://airflow.apache.org/

https://www.astronomer.io/events/roadshow/london/

https://www.astronomer.io/events/roadshow/new-york/

https://www.astronomer.io/events/roadshow/sydney/

https://www.astronomer.io/events/roadshow/san-francisco/

https://www.astronomer.io/events/roadshow/chicago/

Thanks for listening to “The Data Flowcast: Mastering Apache Airflow® for Data Engineering and AI.” If you enjoyed this episode, please leave a 5-star review to help get the word out about the show. And be sure to subscribe so you never miss any of the insightful conversations.

#AI #Automation #Airflow #MachineLearning

  continue reading

68 에피소드

Artwork
icon공유
 
Manage episode 485580771 series 2948506
The Data Flowcast에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 The Data Flowcast 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.

Efficient orchestration and maintainability are crucial for data engineering at scale. Gil Reich, Data Developer for Data Science at Wix, shares how his team reduced code duplication, standardized pipelines, and improved Airflow task orchestration using a Python-based framework built within the data science team.

In this episode, Gil explains how this internal framework simplifies DAG creation, improves documentation accuracy, and enables consistent task generation for machine learning pipelines. He also shares lessons from complex DAG optimization and maintaining testable code.

Key Takeaways:

(03:23) Code duplication creates long-term problems.

(08:16) Frameworks bring order to complex pipelines.

(09:41) Shared functions cut down repetitive code.

(17:18) Auto-generated docs stay accurate by design.

(22:40) On-demand DAGs support real-time workflows.

(25:08) Task-level sensors improve run efficiency.

(27:40) Combine local runs with automated tests.

(30:09) Clean code helps teams scale faster.

Resources Mentioned:

Gil Reich

https://www.linkedin.com/in/gilreich/

Wix | LinkedIn

https://www.linkedin.com/company/wix-com/

Wix | Website

https://www.wix.com/

DS DAG Framework

https://airflowsummit.org/slides/2024/92-refactoring-dags.pdf

Apache Airflow

https://airflow.apache.org/

https://www.astronomer.io/events/roadshow/london/

https://www.astronomer.io/events/roadshow/new-york/

https://www.astronomer.io/events/roadshow/sydney/

https://www.astronomer.io/events/roadshow/san-francisco/

https://www.astronomer.io/events/roadshow/chicago/

Thanks for listening to “The Data Flowcast: Mastering Apache Airflow® for Data Engineering and AI.” If you enjoyed this episode, please leave a 5-star review to help get the word out about the show. And be sure to subscribe so you never miss any of the insightful conversations.

#AI #Automation #Airflow #MachineLearning

  continue reading

68 에피소드

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