使用Player FM应用程序离线!
Scaling Airflow to 11,000 DAGs Across Three Regions at Intercom with András Gombosi and Paul Vickers
Manage episode 522564525 series 2053958
The evolution of Intercom’s data infrastructure reveals how a well-built orchestration system can scale to serve global needs. With thousands of DAGs powering analytics, AI and customer operations, the team’s approach combines technical depth with organizational insight.
In this episode, András Gombosi, Senior Engineering Manager of Data Infra and Analytics Engineering, and Paul Vickers, Principal Engineer, both at Intercom, share how they built one of the largest Airflow deployments in production and enabled self-serve data platforms across teams.
Key Takeaways:
00:00 Introduction.
04:24 Community input encourages confident adoption of a common platform.
08:50 Self-serve workflows require consistent guardrails and review.
09:25 Internal infrastructure support accelerates scalable deployments.
13:26 Batch LLM processing benefits from a configuration-driven design.
15:20 Standardized development environments enable effective AI-assisted work.
19:58 Applied AI enhances internal analysis and operational enablement.
27:27 Strong test coverage and staged upgrades protect stability.
30:36 Proactive observability and on-call ownership improve outcomes.
Resources Mentioned:
https://www.linkedin.com/in/andrasgombosi/
https://www.linkedin.com/in/paul-vickers-a22b76a3/
Intercom | LinkedIn
https://www.linkedin.com/company/intercom/
Intercom | Website
https://www.intercom.com
https://airflow.apache.org/
https://www.getdbt.com/
https://www.snowflake.com/en/product/features/cortex/
https://www.datadoghq.com/
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
81集单集
Scaling Airflow to 11,000 DAGs Across Three Regions at Intercom with András Gombosi and Paul Vickers
The Data Flowcast: Mastering Apache Airflow ® for Data Engineering and AI
Manage episode 522564525 series 2053958
The evolution of Intercom’s data infrastructure reveals how a well-built orchestration system can scale to serve global needs. With thousands of DAGs powering analytics, AI and customer operations, the team’s approach combines technical depth with organizational insight.
In this episode, András Gombosi, Senior Engineering Manager of Data Infra and Analytics Engineering, and Paul Vickers, Principal Engineer, both at Intercom, share how they built one of the largest Airflow deployments in production and enabled self-serve data platforms across teams.
Key Takeaways:
00:00 Introduction.
04:24 Community input encourages confident adoption of a common platform.
08:50 Self-serve workflows require consistent guardrails and review.
09:25 Internal infrastructure support accelerates scalable deployments.
13:26 Batch LLM processing benefits from a configuration-driven design.
15:20 Standardized development environments enable effective AI-assisted work.
19:58 Applied AI enhances internal analysis and operational enablement.
27:27 Strong test coverage and staged upgrades protect stability.
30:36 Proactive observability and on-call ownership improve outcomes.
Resources Mentioned:
https://www.linkedin.com/in/andrasgombosi/
https://www.linkedin.com/in/paul-vickers-a22b76a3/
Intercom | LinkedIn
https://www.linkedin.com/company/intercom/
Intercom | Website
https://www.intercom.com
https://airflow.apache.org/
https://www.getdbt.com/
https://www.snowflake.com/en/product/features/cortex/
https://www.datadoghq.com/
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
81集单集
所有剧集
×欢迎使用Player FM
Player FM正在网上搜索高质量的播客,以便您现在享受。它是最好的播客应用程序,适用于安卓、iPhone和网络。注册以跨设备同步订阅。