The Data Flowcast: Mastering Apache Airflow ® for Data Engineering and AI

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

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

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 Conferencehttp://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

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

The Future of Airflow Telemetry with Bolke de Bruin

Telemetry has the potential to guide the future of Airflow, but only if it’s implemented transparently and with community trust. In this episode, we’re joined by Bolke de Bruin, Director at Metyis and a long-time Airflow PMC member. Bolke discusses how telemetry has been handled in the past, why it matters now and what it will take to get it right.Key Takeaways:(03:20) The role of foundations in establishing credibility and sustainability.(04:52) Why data collection is critical to open-source project direction.(07:24) Lessons learned from previous approaches to user data collection.(10:23) The current state of telemetry in the project.(10:53) Community trust as a prerequisite for technical implementation.(12:54) The importance of managing sensitive data within trusted ecosystems.(16:37) Ethical considerations in balancing participation and access.(18:45) Forward-looking ideas for improving workflow design and usability.Resources Mentioned:Bolke de Bruinhttps://www.linkedin.com/in/bolke/Metyis | LinkedInhttps://www.linkedin.com/company/metyis/Metyis | Websitehttp://www.metyis.comApache Airflowhttps://airflow.apache.org/Airflow Summithttps://airflowsummit.org/Airflow Dev Listhttps://lists.apache.org/list.html?dev@airflow.apache.orghttps://www.astronomer.io/events/roadshow/london/ https://www.astronomer.io/events/roadshow/new-york/ https://www.astronomer.io/events/roadshow/sydney/ https://www.astronomer.io/events/roadshow/san-francisco/ https://www.astronomer.io/events/roadshow/chicago/ 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