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

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Manage episode 484114308 series 2948506
Content provided by The Data Flowcast. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by The Data Flowcast or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.

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/

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

57 episodes

Artwork
iconShare
 
Manage episode 484114308 series 2948506
Content provided by The Data Flowcast. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by The Data Flowcast or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.

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/

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

57 episodes

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