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

Latest Episodes:

Scaling On-Prem Airflow With 2,000 DAGs at Numberly with Sébastien Crocquevieille Aug 21, 2025

Show notes

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 Crocquevieille

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

Numberly | LinkedIn

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

Numberly | Website

https://numberly.com/

Apache Airflow

https://airflow.apache.org/

Grafana

https://grafana.com/

Apache Kafka

https://kafka.apache.org/

Helm Chart for Apache Airflow

https://airflow.apache.org/docs/helm-chart/stable/index.html

Kubernetes

https://kubernetes.io/

GitLab

https://about.gitlab.com/

KubernetesPodOperator – Airflow

https://airflow.apache.org/docs/apache-airflow-providers-cncf-kubernetes/stable/operators.html

Beyond Analytics Conference

https://astronomer.io/beyond/dataflowcast

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

How Moniepoint Group Uses Airflow for Exposure Monitoring with Adeolu Adegboye Aug 14, 2025

Show notes

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 Adegboye

https://www.linkedin.com/in/adeolu-adegboye/

Moniepoint Group | LinkedIn

https://www.linkedin.com/company/moniepoint-inc/

Moniepoint Group | Website

https://www.moniepoint.com

Apache Airflow

https://airflow.apache.org/

ClickHouse

https://clickhouse.com/

Grafana

https://grafana.com/

Beyond Analytics Conference

https://astronomer.io/beyond/dataflowcast

#AI #Automation #Airflow #MachineLearning

Inside Bosch’s Airflow 3 Revolution: Remote Execution with Jens Scheffler Aug 07, 2025

Show notes

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 Scheffler

https://www.linkedin.com/in/jens-scheffler/

Bosch | LinkedIn

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

Bosch | Website

https://www.bosch.com/

Apache Airflow

https://airflow.apache.org/

Edge Executor (Edge3 Provider Package)

https://airflow.apache.org/docs/apache-airflow/stable/core-concepts/executor/index.html

Astronomer’s Astro Executor

https://www.astronomer.io/docs/astro/astro-executor/

Beyond Analytics Conference

https://astronomer.io/beyond/dataflowcast

#AI #Automation #Airflow #MachineLearning

Inside Modern Data Infrastructure at Massdriver with Cory O’Daniel and Jake Ferriero Jul 31, 2025

Show notes

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/

#AI #Automation #Airflow #MachineLearning

The Future of Airflow Telemetry with Bolke de Bruin Jul 17, 2025

Show notes

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 Bruin

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

Metyis | LinkedIn

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

Metyis | Website

http://www.metyis.com

Apache Airflow

https://airflow.apache.org/

Airflow Summit

https://airflowsummit.org/

Airflow Dev List

https://lists.apache.org/list.html?dev@airflow.apache.org

https://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/

#AI #Automation #Airflow #MachineLearning

Transforming the Airflow UI for Cloudera’s Users with Shubham Raj Jul 10, 2025

Show notes

Contributing to open-source projects can be daunting, but it can also unlock unexpected innovation. This episode showcases how one engineer’s journey with Apache Airflow led to impactful UI enhancements and infrastructure solutions at scale. Shubham Raj, Software Engineer II at Cloudera, shares how his team built a drag-and-drop DAG editor for non-coders, contributions which helped shape the Airflow 3.0 Ul and introduced features like external XCom control and bulk APls.

Key Takeaways:

(02:30) Day-to-day responsibilities building platforms that simplify orchestration.

(05:27) Factors that make onboarding into large open-source projects accessible.

(07:35) The value of improved user interfaces for task state visibility and control.

(09:49) Enabling faster debugging by exposing internal data through APIs.

(13:00) Balancing frontend design goals with backend functionality.

(14:19) Creating workflow editors that lower the barrier to entry.

(16:54) Supporting a variety of task types within a visual DAG builder.

(19:32) Common infrastructure challenges faced by orchestration users.

(20:37) Addressing dependency management across distributed environments.

Resources Mentioned:

Shubham Raj

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

Cloudera | LinkedIn

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

Cloudera | Website

https://www.cloudera.com/

Apache Airflow

https://airflow.apache.org/

2023 Airflow Summit

https://airflowsummit.org/

https://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/

#AI #Automation #Airflow #MachineLearning

Streamlining Thousands of Data Pipelines at Lyft with Yunhao Qing Jul 07, 2025

Show notes

Managing data pipelines at scale is not just a technical challenge. It is also an organizational one. At Lyft, success means empowering dozens of teams to build with autonomy while enforcing governance and best practices across thousands of workflows.

In this episode, we speak with Yunhao Qing, Software Engineer at Lyft, about building a governed data-engineering platform powered by Airflow that balances flexibility, standardization and scale.

Key Takeaways:

(03:17) Supporting internal teams with a centralized orchestration platform.

(04:54) Migrating to a managed service to reduce infrastructure overhead.

(06:04) Embedding platform-level governance into custom components.

(08:02) Consolidating and regulating the creation of custom code.

(09:48) Identifying and correcting inefficient workflow patterns.

(11:17) Replacing manual workarounds with native platform features.

(14:32) Preparing teams for major version upgrades.

(16:03) Leveraging asset-based scheduling for smarter triggers.

(18:13) Envisioning GenAI and semantic search for future productivity.

Resources Mentioned:

Yunhao Qing

https://www.linkedin.com/in/yunhao-qing

Lyft | LinkedIn

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

Lyft | Website

https://www.lyft.com/

Apache Airflow

https://airflow.apache.org/

Astronomer

https://www.astronomer.io/

Kubernetes

https://kubernetes.io/

https://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/

#AI #Automation #Airflow #MachineLearning

Transforming Customer Education in Data Engineering at Astronomer with Marc Lamberti Jun 26, 2025

Show notes

Understanding the complexities of Apache Airflow can be daunting for newcomers and seasoned data engineers. But with the right guidance, mastering the tool becomes an achievable milestone.

In this episode, Marc Lamberti, Head of Customer Education at Astronomer, joins us to share his journey from Udemy instructor to driving education at Astronomer, and how he's helping over 100,000 learners demystify Airflow.

Key Takeaways:

(02:36) Early exposure to Airflow while addressing inefficiencies in data workflows.

(04:10) Common barriers to implementing open source tools in enterprise settings.

(06:18) The shift from part-time teaching to a full-time focus on Airflow education.

(07:53) A modular, guided approach to structuring educational content.

(09:57) The value of highlighting underused Airflow features for broader adoption.

(12:35) Certifications as a method to assess readiness and uncover knowledge gaps.

(13:25) Coverage of essential Airflow concepts in the Fundamentals exam.

(16:07) The DAG Authoring exam’s emphasis on practical, advanced features.

(20:08) A call for more visible integration of Airflow with AI workflows.

Resources Mentioned:

Marc Lamberti

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

Astronomer | LinkedIn

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

Astronomer Academy

https://academy.astronomer.io/

Airflow Fundamentals Certification

https://www.astronomer.io/certification/

DAG Authoring Certification

https://academy.astronomer.io/plan/astronomer-certification-dag-authoring-for-apache-airflow-exam

The Complete Hands-On Introduction to Airflow

https://www.udemy.com/course/the-complete-hands-on-course-to-master-apache-airflow/?utm_source=adwords&utm_medium=udemyads&utm_campaign=Search_DSA_Beta_Prof_la.EN_cc.ROW-English&campaigntype=Search&portfolio=ROW-English&language=EN&product=Course&test=&audience=DSA&topic=&priority=Beta&utm_content=deal4584&utm_term=_._ag_162511579404_._ad_696197165418_._kw__._de_c_._dm__._pl__._ti_dsa-1677053911088_._li_9061346_._pd__._&matchtype=&gad_source=1&gad_campaignid=21168154305&gbraid=0AAAAADROdO3MpljfP-gssiYSmDEPdhZV9&gclid=Cj0KCQjw097CBhDIARIsAJ3-nxdjZA6G5-Y0-akk6Huksy2PLb04t92J4iNfUSIbMdrSAla_tb-o2N8aArOeEALw_wcB&couponCode=PMNVD3025

https://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/

#AI #Automation #Airflow #MachineLearning

Embracing Data Mesh and SQL Sensors for Scalable Workflows at lastminute.com with Alberto Crespi Jun 20, 2025

Show notes

The flexibility of Airflow plays a pivotal role in enabling decentralized data architectures and empowering cross-functional teams.

In this episode, we speak with Alberto Crespi, Data Architect at lastminute.com, who shares how his team scales Airflow across 12 teams while supporting both vertical and horizontal structures under a data mesh approach.

Key Takeaways:

(02:17) Defining responsibilities within data architecture teams.

(04:15) Consolidating multiple orchestrators into a single solution.

(07:00) Scaling Airflow environments with shared infrastructure and DevOps practices.

(10:59) Managing dependencies and readiness using SQL sensors.

(14:23) Enhancing visibility and response through Slack-integrated monitoring.

(19:28) Extending Airflow’s flexibility to run legacy systems.

(22:28) Integrating transformation tools into orchestrated pipelines.

(25:54) Enabling non-engineers to contribute to pipeline development.

(27:33) Fostering adoption through collaboration and communication.

Resources Mentioned:

Alberto Crespi

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

lastminute.com | Website

https://lastminute.com

Apache Airflow

https://airflow.apache.org/

dbt Labs

https://www.getdbt.com/

Astronomer Cosmos

https://github.com/astronomer/astronomer-cosmos

GitLab Slack

https://slack.com/

Kubernetes

https://kubernetes.io/

Confluence

https://www.atlassian.com/software/confluence

Slack

https://slack.com/

https://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/

#AI #Automation #Airflow #MachineLearning

The AI-Ready Pipeline: Reimagining Airflow at Veyer® Logistics with Anu Pabla Jun 12, 2025

Show notes

Innovation in orchestration is redefining how engineers approach both traditional ETL pipelines and emerging AI workloads. Understanding how to harness Airflow’s flexibility and observability is essential for teams navigating today’s evolving data landscape.

In this episode, Anu Pabla, Principal Engineer at The ODP Corporation, joins us to discuss her journey from legacy orchestration patterns to AI-native pipelines and why she sees Airflow as the future of AI workload orchestration.

Key Takeaways:

(03:43) Engaging with external technology communities fosters innovation.

(05:05) Mentoring early-career engineers builds confidence in a complex tech landscape.

(07:51) Orchestration patterns continue to evolve with modern data needs.

(08:41) Managing AI workflows requires structured and flexible orchestration.

(10:35) High-quality, meaningful data remains foundational across use cases.

(15:08) Community-driven open source tools offer lasting value.

(16:59) Self-healing systems support both legacy and AI pipelines.

(20:20) Orchestration platforms can drive future AI-native workloads.

Resources Mentioned:

Anu Pabla

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