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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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Inside Modern Data Infrastructure at Massdriver with Cory O’Daniel and Jake Ferriero

31:24
 
اشتراک گذاری
 

Manage episode 497520222 series 2948506
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

Managing modern data platforms means navigating a web of complex infrastructure, competing team needs and evolving security standards. For data teams to truly thrive, infrastructure must become both accessible and compliant without sacrificing velocity or reliability.

In this episode, we’re joined by Cory O’Daniel, CEO and Co-Founder at Massdriver, and Jacob Ferriero, Senior Software Engineer at Astronomer, to unpack what it takes to make data platform engineering scalable, sustainable and secure. They share lessons from years of experience working with DevOps, ML teams and platform engineers and discuss how Airflow fits into the orchestration layer of today’s data stacks.

Key Takeaways:

(03:27) Making infrastructure accessible without deep ops knowledge.

(07:23) Distinct personas and responsibilities across data teams.

(09:53) Infrastructure hurdles specific to ML workloads.

(11:13) Compliance and governance shaping platform design.

(13:27) Tooling mismatches between teams cause friction.

(15:13) Airflow’s orchestration role within broader system architecture.

(22:10) Creating reusable infrastructure patterns for consistency.

(24:13) Enabling secure access without slowing down development.

(26:55) Opportunities to improve Airflow with event-driven and reliability tooling.

Resources Mentioned:

Cory O’Daniel

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

Massdriver | LinkedIn

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

Massdriver | Website

https://www.massdriver.cloud/

Jacob Ferriero

https://www.linkedin.com/in/jacob-ferriero/

Astronomer

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

Apache Airflow

https://airflow.apache.org/

Prequel

https://www.prequel.co/

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

70 قسمت

Artwork
iconاشتراک گذاری
 
Manage episode 497520222 series 2948506
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

Managing modern data platforms means navigating a web of complex infrastructure, competing team needs and evolving security standards. For data teams to truly thrive, infrastructure must become both accessible and compliant without sacrificing velocity or reliability.

In this episode, we’re joined by Cory O’Daniel, CEO and Co-Founder at Massdriver, and Jacob Ferriero, Senior Software Engineer at Astronomer, to unpack what it takes to make data platform engineering scalable, sustainable and secure. They share lessons from years of experience working with DevOps, ML teams and platform engineers and discuss how Airflow fits into the orchestration layer of today’s data stacks.

Key Takeaways:

(03:27) Making infrastructure accessible without deep ops knowledge.

(07:23) Distinct personas and responsibilities across data teams.

(09:53) Infrastructure hurdles specific to ML workloads.

(11:13) Compliance and governance shaping platform design.

(13:27) Tooling mismatches between teams cause friction.

(15:13) Airflow’s orchestration role within broader system architecture.

(22:10) Creating reusable infrastructure patterns for consistency.

(24:13) Enabling secure access without slowing down development.

(26:55) Opportunities to improve Airflow with event-driven and reliability tooling.

Resources Mentioned:

Cory O’Daniel

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

Massdriver | LinkedIn

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

Massdriver | Website

https://www.massdriver.cloud/

Jacob Ferriero

https://www.linkedin.com/in/jacob-ferriero/

Astronomer

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

Apache Airflow

https://airflow.apache.org/

Prequel

https://www.prequel.co/

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

70 قسمت

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