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How Vibrant Planet's Self-Healing Pipelines Revolutionize Data Processing

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Manage episode 425923934 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://player.fm/legal.
Discover the cutting-edge methods Vibrant Planet uses to revolutionize geospatial data processing and resource management. In this episode, we delve into the intricacies of scaling geospatial data processing and resource allocation with experts from Vibrant Planet. Joining us are Cyrus Dukart, Engineering Lead, and David Sacerdote, Staff Software Engineer, who share their innovative approaches to handling large datasets and optimizing resource use in Airflow. Key Takeaways: (00:00) Inefficiencies in resource allocation. (03:00) Scientific validity of sharded results. (05:53) Tech-based solutions for resource management. (06:11) Retry callback process for resource allocation. (08:00) Running database queries for resource needs. (10:05) Importance of remembering resource usage. (13:51) Generating resource predictions. (14:44) Custom task decorator for resource management. (20:28) Massive resource usage gap in sharded data. (21:14) Fail-fast model for long-running tasks. Resources Mentioned: Cyrus Dukart - https://www.linkedin.com/in/cyrus-dukart-6561482/ David Sacerdote - https://www.linkedin.com/in/davidsacerdote/ Vibrant Planet - https://www.linkedin.com/company/vibrant-planet/ Apache Airflow - https://airflow.apache.org/ Kubernetes - https://kubernetes.io/ Vibrant Planet - https://vibrantplanet.net/ Thanks for listening to The Data Flowcast: Mastering Airflow for Data Engineering & 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
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22 episodes

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Manage episode 425923934 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://player.fm/legal.
Discover the cutting-edge methods Vibrant Planet uses to revolutionize geospatial data processing and resource management. In this episode, we delve into the intricacies of scaling geospatial data processing and resource allocation with experts from Vibrant Planet. Joining us are Cyrus Dukart, Engineering Lead, and David Sacerdote, Staff Software Engineer, who share their innovative approaches to handling large datasets and optimizing resource use in Airflow. Key Takeaways: (00:00) Inefficiencies in resource allocation. (03:00) Scientific validity of sharded results. (05:53) Tech-based solutions for resource management. (06:11) Retry callback process for resource allocation. (08:00) Running database queries for resource needs. (10:05) Importance of remembering resource usage. (13:51) Generating resource predictions. (14:44) Custom task decorator for resource management. (20:28) Massive resource usage gap in sharded data. (21:14) Fail-fast model for long-running tasks. Resources Mentioned: Cyrus Dukart - https://www.linkedin.com/in/cyrus-dukart-6561482/ David Sacerdote - https://www.linkedin.com/in/davidsacerdote/ Vibrant Planet - https://www.linkedin.com/company/vibrant-planet/ Apache Airflow - https://airflow.apache.org/ Kubernetes - https://kubernetes.io/ Vibrant Planet - https://vibrantplanet.net/ Thanks for listening to The Data Flowcast: Mastering Airflow for Data Engineering & 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

22 episodes

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