The Data Flowcast: Mastering Apache Airflow ® for Data Engineering and AI

Modernizing Legacy Data Systems With Airflow at Procter & Gamble with Adonis Castillo Cordero


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Legacy architecture and AI workloads pose unique challenges at scale, especially in a global enterprise with complex data systems. In this episode, we explore strategies to proactively monitor and optimize pipelines while minimizing downstream failures.


Adonis Castillo Cordero, Senior Automation Manager at Procter & Gamble, joins us to share actionable best practices for dependency mapping, anomaly detection and architecture simplification using Apache Airflow.


Key Takeaways:


(03:13) Integrating legacy data systems into modern architecture.

(05:51) Designing workflows for real-time data processing.

(07:57) Mapping dependencies early to avoid pipeline failures.

(09:02) Building automated monitoring into orchestration frameworks.

(12:09) Detecting anomalies to prevent performance bottlenecks.

(15:24) Monitoring data quality to catch silent failures.

(17:02) Prioritizing responses based on impact severity.

(18:55) Simplifying dashboards to highlight critical metrics.


Resources Mentioned:


Adonis Castillo Cordero

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


Procter & Gamble | LinkedIn

https://www.linkedin.com/company/procter-and-gamble/


Procter & Gamble | Website

http://www.pg.com


Apache Airflow

https://airflow.apache.org/


OpenLineage

https://openlineage.io/


Azure Monitor

https://azure.microsoft.com/en-us/products/monitor/


AWS Lookout for Metrics

https://aws.amazon.com/lookout-for-metrics/


Monte Carlo

https://www.montecarlodata.com/


Great Expectations

https://greatexpectations.io/


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/





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#AI #Automation #Airflow #MachineLearning

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