Building the Future of Airflow Execution at Astronomer with Ian Buss and Piotr Chomiak
The evolution of orchestration in Airflow continues with innovations that address both scalability and security. From improving executor reliability to enabling remote execution, these advancements reshape how organizations manage data pipelines.In this episode, we’re joined by Ian Buss, Principal Software Engineer at Astronomer, and Piotr Chomiak, Principal Product Manager at Astronomer, who share insights into the Astro Executor and remote execution.Key Takeaways:00:00 Introduction.04:13 How product leadership drives scalability for enterprise needs.08:23 Architectural changes that improve reliability and remove bottlenecks.10:15 Metrics that enhance visibility into system performance.12:54 The role of remote execution in addressing security requirements.15:56 Differences between open-source solutions and managed offerings.19:04 Broad industry adoption and applicability of remote execution.20:39 Future advancements in language support and multi-tenancy.Resources Mentioned:Ian Busshttps://www.linkedin.com/in/ian-buss/Piotr Chomiakhttps://www.linkedin.com/in/piotr-chomiak-b1955624/Astronomer | Websitehttps://www.astronomer.ioApache Airflowhttps://airflow.apache.org/Airflow Slack Communityhttps://airflow.apache.org/community/Beyond Analytics conferencehttps://astronomer.io/beyond/dataflowcastThanks 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
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Scaling On-Prem Airflow With 2,000 DAGs at Numberly with Sébastien Crocquevieille
Scaling 2,000+ data pipelines isn’t easy. But with the right tools and a self-hosted mindset, it becomes achievable.In this episode, Sébastien Crocquevieille, Data Engineer at Numberly, unpacks how the team scaled their on-prem Airflow setup using open-source tooling and Kubernetes. We explore orchestration strategies, UI-driven stakeholder access and Airflow’s evolving features.Key Takeaways:00:00 Introduction.02:13 Overview of the company’s operations and global presence.04:00 The tech stack and structure of the data engineering team.04:24 Running nearly 2,000 DAGs in production using Airflow.05:42 How Airflow’s UI empowers stakeholders to self-serve and troubleshoot.07:05 Details on the Kubernetes-based Airflow setup using Helm charts.09:31 Transition from GitSync to NFS for DAG syncing due to performance issues.14:11 Making every team member Airflow-literate through local installation.17:56 Using custom libraries and plugins to extend Airflow functionality.Resources Mentioned:Sébastien Crocquevieillehttps://www.linkedin.com/in/scroc/Numberly | LinkedInhttps://www.linkedin.com/company/numberly/Numberly | Websitehttps://numberly.com/Apache Airflowhttps://airflow.apache.org/Grafanahttps://grafana.com/Apache Kafkahttps://kafka.apache.org/Helm Chart for Apache Airflowhttps://airflow.apache.org/docs/helm-chart/stable/index.htmlKuberneteshttps://kubernetes.io/GitLabhttps://about.gitlab.com/KubernetesPodOperator – Airflowhttps://airflow.apache.org/docs/apache-airflow-providers-cncf-kubernetes/stable/operators.htmlBeyond Analytics Conferencehttps://astronomer.io/beyond/dataflowcastThanks 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
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How Moniepoint Group Uses Airflow for Exposure Monitoring with Adeolu Adegboye
Managing financial data at scale requires precise orchestration and proactive monitoring to maintain operational efficiency.In this episode, we are joined by Adeolu Adegboye, Data Engineer at Moniepoint Group, who shares how his team uses data pipelines and workflow automation to manage high volumes of transactions, ensure timely alerts and support diverse stakeholders across the business.Key Takeaways:(00:00) Introduction. (02:48) The role of data engineering in supporting all business operations.(04:17) Leveraging workflow orchestration to manage daily processes.(05:20) Proactively monitoring for anomalies to prevent potential issues.(08:12) Simplifying complex insights for non-technical teams.(13:01) Improving efficiency through dynamic and parallel workflows.(14:19) Optimizing system performance to handle large-scale operations.(17:19) Exploring creative and innovative uses for workflow automation.Resources Mentioned:Adeolu Adegboyehttps://www.linkedin.com/in/adeolu-adegboye/Moniepoint Group | LinkedInhttps://www.linkedin.com/company/moniepoint-inc/Moniepoint Group | Websitehttps://www.moniepoint.comApache Airflowhttps://airflow.apache.org/ClickHousehttps://clickhouse.com/Grafanahttps://grafana.com/Beyond Analytics Conferencehttps://astronomer.io/beyond/dataflowcastThanks 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
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Inside Bosch’s Airflow 3 Revolution: Remote Execution with Jens Scheffler
The evolution of Airflow has reached a milestone with the introduction of remote execution in Airflow 3, enabling flexible orchestration across distributed environments.In this episode, Jens Scheffler, Test Execution Cluster Technical Architect at Bosch, shares insights on how his team’s need for large-scale, cross-environment testing influenced the development of the Edge Executor and shaped this major release.Key Takeaways:(02:39) The role of remote execution in supporting large-scale testing needs.(04:44) How community support contributed to the Edge Executor’s development.(08:41) Navigating network and infrastructure limitations within secure environments.(13:25) Transitioning from database-heavy processes to an API-driven model.(14:16) How the new task SDK in Airflow 3 improves distributed task execution.(16:54) What is required to set up and configure the Edge Executor.(19:36) Managing multiple queues to optimize tasks across different environments.(23:30) Examples of extreme distance use cases for edge execution.Resources Mentioned:Jens Schefflerhttps://www.linkedin.com/in/jens-scheffler/Bosch | LinkedInhttps://www.linkedin.com/company/bosch/Bosch | Websitehttps://www.bosch.com/Apache Airflowhttps://airflow.apache.org/Edge Executor (Edge3 Provider Package)https://airflow.apache.org/docs/apache-airflow/stable/core-concepts/executor/index.htmlAstronomer’s Astro Executorhttps://www.astronomer.io/docs/astro/astro-executor/Beyond Analytics Conferencehttps://astronomer.io/beyond/dataflowcastThanks 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
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Inside Modern Data Infrastructure at Massdriver with Cory O’Daniel and Jake Ferriero
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’Danielhttps://www.linkedin.com/in/coryodaniel/Massdriver | LinkedInhttps://www.linkedin.com/company/massdriver/Massdriver | Websitehttps://www.massdriver.cloud/Jacob Ferrierohttps://www.linkedin.com/in/jacob-ferriero/Astronomerhttps://www.linkedin.com/company/astronomer/Apache Airflowhttps://airflow.apache.org/Prequelhttps://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
About The Data Flowcast: Mastering Apache Airflow ® for Data Engineering and AI
Welcome to The Data Flowcast: Mastering Apache Airflow ® for Data Engineering and AI— the podcast where we keep you up to date with insights and ideas propelling the Airflow community forward.
Join us each week, as we explore the current state, future and potential of Airflow with leading thinkers in the community, and discover how best to leverage this workflow management system to meet the ever-evolving needs of data engineering and AI ecosystems.
Podcast Webpage: https://www.astronomer.io/podcast/
Listen to The Data Flowcast: Mastering Apache Airflow ® for Data Engineering and AI, The AI Daily Brief (Formerly The AI Breakdown): Artificial Intelligence News and Analysis and many other podcasts from around the world with the radio.net app
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