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Scaling Geospatial Workflows With Airflow at Overture Maps Foundation and Wherobots with Alex Iannicelli and Daniel Smith

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Manage episode 512498387 series 2053958
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.

Using Airflow to orchestrate geospatial data pipelines unlocks powerful efficiencies for data teams. The combination of scalable processing and visual observability streamlines workflows, reduces costs and improves iteration speed.

In this episode, Alex Iannicelli, Staff Software Engineer at Overture Maps Foundation, and Daniel Smith, Senior Solutions Architect at Wherobots, join us to discuss leveraging Apache Airflow and Apache Sedona to process massive geospatial datasets, build reproducible pipelines and orchestrate complex workflows across platforms.

Key Takeaways:

00:00 Introduction.

03:22 How merging multiple data sources supports comprehensive datasets.

04:20 The value of flexible configurations for running pipelines on different platforms.

06:35 Why orchestration tools are essential for handling continuous data streams.

09:45 The importance of observability for monitoring progress and troubleshooting issues.

11:30 Strategies for processing large, complex datasets efficiently.

13:27 Expanding orchestration beyond core pipelines to automate frequent tasks.

17:02 Advantages of using open-source operators to simplify integration and deployment.

20:32 Desired improvements in orchestration tools for usability and workflow management.

Resources Mentioned:

Alex Iannicelli

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

Overture Maps Foundation | LinkedIn

https://www.linkedin.com/company/overture-maps-foundation/

Overture Maps Foundation | Website

https://overturemaps.org

Daniel Smith

https://www.linkedin.com/in/daniel-smith-analyst/

Wherobots | LinkedIn

https://www.linkedin.com/company/wherobots

Wherobots | Website

https://www.wherobots.com

Apache Airflow

https://airflow.apache.org/

Apache Sedona

https://sedona.apache.org/

Github repo

https://github.com/wherobots/airflow-providers-wherobots

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

75 episodes

Artwork
iconShare
 
Manage episode 512498387 series 2053958
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.

Using Airflow to orchestrate geospatial data pipelines unlocks powerful efficiencies for data teams. The combination of scalable processing and visual observability streamlines workflows, reduces costs and improves iteration speed.

In this episode, Alex Iannicelli, Staff Software Engineer at Overture Maps Foundation, and Daniel Smith, Senior Solutions Architect at Wherobots, join us to discuss leveraging Apache Airflow and Apache Sedona to process massive geospatial datasets, build reproducible pipelines and orchestrate complex workflows across platforms.

Key Takeaways:

00:00 Introduction.

03:22 How merging multiple data sources supports comprehensive datasets.

04:20 The value of flexible configurations for running pipelines on different platforms.

06:35 Why orchestration tools are essential for handling continuous data streams.

09:45 The importance of observability for monitoring progress and troubleshooting issues.

11:30 Strategies for processing large, complex datasets efficiently.

13:27 Expanding orchestration beyond core pipelines to automate frequent tasks.

17:02 Advantages of using open-source operators to simplify integration and deployment.

20:32 Desired improvements in orchestration tools for usability and workflow management.

Resources Mentioned:

Alex Iannicelli

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

Overture Maps Foundation | LinkedIn

https://www.linkedin.com/company/overture-maps-foundation/

Overture Maps Foundation | Website

https://overturemaps.org

Daniel Smith

https://www.linkedin.com/in/daniel-smith-analyst/

Wherobots | LinkedIn

https://www.linkedin.com/company/wherobots

Wherobots | Website

https://www.wherobots.com

Apache Airflow

https://airflow.apache.org/

Apache Sedona

https://sedona.apache.org/

Github repo

https://github.com/wherobots/airflow-providers-wherobots

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

75 episodes

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