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محتوای ارائه شده توسط The Data Flowcast. تمام محتوای پادکست شامل قسمت‌ها، گرافیک‌ها و توضیحات پادکست مستقیماً توسط The Data Flowcast یا شریک پلتفرم پادکست آن‌ها آپلود و ارائه می‌شوند. اگر فکر می‌کنید شخصی بدون اجازه شما از اثر دارای حق نسخه‌برداری شما استفاده می‌کند، می‌توانید روندی که در اینجا شرح داده شده است را دنبال کنید.https://fa.player.fm/legal
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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
محتوای ارائه شده توسط The Data Flowcast. تمام محتوای پادکست شامل قسمت‌ها، گرافیک‌ها و توضیحات پادکست مستقیماً توسط The Data Flowcast یا شریک پلتفرم پادکست آن‌ها آپلود و ارائه می‌شوند. اگر فکر می‌کنید شخصی بدون اجازه شما از اثر دارای حق نسخه‌برداری شما استفاده می‌کند، می‌توانید روندی که در اینجا شرح داده شده است را دنبال کنید.https://fa.player.fm/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

74 قسمت

Artwork
iconاشتراک گذاری
 
Manage episode 512498387 series 2053958
محتوای ارائه شده توسط The Data Flowcast. تمام محتوای پادکست شامل قسمت‌ها، گرافیک‌ها و توضیحات پادکست مستقیماً توسط The Data Flowcast یا شریک پلتفرم پادکست آن‌ها آپلود و ارائه می‌شوند. اگر فکر می‌کنید شخصی بدون اجازه شما از اثر دارای حق نسخه‌برداری شما استفاده می‌کند، می‌توانید روندی که در اینجا شرح داده شده است را دنبال کنید.https://fa.player.fm/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

74 قسمت

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