Inside Airflow 3: Redefining Data Engineering with Vikram Koka

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

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Crimes of the Times

1
The Dungeon: Inside Men’s Central Jail 30:16

לפני 14 ימים30:16

הפעל מאוחר יותר

רשימות

לייק

אהבתי

30:16

A young FBI agent named Leah Marx arrives in Los Angeles and receives a tip in 2010 about brutal conditions at Men’s Central Jail downtown. Such complaints have gone nowhere for years, since they pit the allegations of inmates against the word of jail deputies. But she finds informants, including a wily bank robber, Anthony Brown, who is facing life in prison and is willing to help. She reflects on a family tragedy that informs her perspective and fuels her sense of mission. Meanwhile, an ambitious young jailer named James Sexton works his way through the ranks, trying to overcome his image as a “brass baby,” the son of a prominent law officer, while navigating a complicated agency where loyalty is a prime value. That jail was notorious for violence and neglect, and outside investigations had rarely gained traction. By entering Men’s Central Jail, the FBI was challenging a department that had long resisted oversight. The series is reported and hosted by Pulitzer Prize-winning journalist Chris Goffard, best known for his work on Dirty John. Topics in this episode include: Operation Pandora’s Box, Anthony Brown informant, James Sexton, Los Angeles County Jail scandal, FBI investigation, Sheriff Lee Baca.…

לפני שנה 30:08

MP3•בית הפרקים

Data orchestration is evolving faster than ever and Apache Airflow 3 is set to revolutionize how enterprises handle complex workflows. In this episode, we dive into the exciting advancements with Vikram Koka, Chief Strategy Officer at Astronomer and PMC Member at The Apache Software Foundation. Vikram shares his insights on the evolution of Airflow and its pivotal role in shaping modern data-driven workflows, particularly with the upcoming release of Airflow 3.

Key Takeaways:

(02:36) Vikram leads Astronomer’s engineering and open-source teams for Airflow.

(05:26) Airflow enables reliable data ingestion and curation.

(08:17) Enterprises use Airflow for mission-critical data pipelines.

(11:08) Airflow 3 introduces major architectural updates.

(13:58) Multi-cloud and edge deployments are supported in Airflow 3.

(16:49) Event-driven scheduling makes Airflow more dynamic.

(19:40) Tasks in Airflow 3 can run in any language.

(22:30) Multilingual task support is crucial for enterprises.

(25:21) Data assets and event-based integration enhance orchestration.

(28:12) Community feedback plays a vital role in Airflow 3.

Resources Mentioned:

Vikram Koka -

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

Astronomer -

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

The Apache Software Foundation LinkedIn -

https://www.linkedin.com/company/the-apache-software-foundation/

Apache Airflow LinkedIn -

https://www.linkedin.com/company/apache-airflow/

Apache Airflow -

https://airflow.apache.org/

Astronomer -

https://www.astronomer.io/

The Apache Software Foundation -

https://www.apache.org/

Join the Airflow slack and/or Dev list -

https://airflow.apache.org/community/

Apache Airflow Survey -

https://astronomer.typeform.com/airflowsurvey24

Thanks for listening to The Data Flowcast: Mastering Airflow for Data Engineering & 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

71 פרקים

#Tech #Airflow

Inside Airflow 3: Redefining Data Engineering with Vikram Koka

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

published לפני שנה

שתפו

MP3•בית הפרקים

Key Takeaways:

(02:36) Vikram leads Astronomer’s engineering and open-source teams for Airflow.

(05:26) Airflow enables reliable data ingestion and curation.

(08:17) Enterprises use Airflow for mission-critical data pipelines.

(11:08) Airflow 3 introduces major architectural updates.

(13:58) Multi-cloud and edge deployments are supported in Airflow 3.

(16:49) Event-driven scheduling makes Airflow more dynamic.

(19:40) Tasks in Airflow 3 can run in any language.

(22:30) Multilingual task support is crucial for enterprises.

(25:21) Data assets and event-based integration enhance orchestration.

(28:12) Community feedback plays a vital role in Airflow 3.

Resources Mentioned:

Vikram Koka -

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

Astronomer -

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

The Apache Software Foundation LinkedIn -

https://www.linkedin.com/company/the-apache-software-foundation/

Apache Airflow LinkedIn -

https://www.linkedin.com/company/apache-airflow/

Apache Airflow -

https://airflow.apache.org/

Astronomer -

https://www.astronomer.io/

The Apache Software Foundation -

https://www.apache.org/

Join the Airflow slack and/or Dev list -

https://airflow.apache.org/community/

Apache Airflow Survey -

https://astronomer.typeform.com/airflowsurvey24

#AI #Automation #Airflow #MachineLearning

71 פרקים

#Tech #Airflow

כל הפרקים

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How Astronomer Turns Proactive Monitoring Into Customer Success with Collin McNulty 25:34

לפני 5 ימים25:34

25:34

The evolution of Airflow continues to shape data orchestration and monitoring strategies. Leveraging it beyond traditional ETL use cases opens powerful new possibilities for proactive support and internal operations. In this episode, we are joined by Collin McNulty , Sr. Director of Global Support at Astronomer , who shares insights from his journey into data engineering and the lessons learned from leading Astronomer’s Customer Reliability Engineering (CRE) team. Key Takeaways: 00:00 Introduction. 03:07 Lessons learned in adapting to major platform transitions. 05:18 How proactive monitoring improves reliability and customer experience. 08:10 Using automation to enhance internal support processes. 12:09 Why keeping systems current helps avoid unnecessary issues. 15:14 Approaches that strengthen system reliability and efficiency. 18:46 Best practices for simplifying complex orchestration dependencies. 23:24 Anticipated innovations that expand orchestration capabilities. Resources Mentioned: Collin McNulty https://www.linkedin.com/in/collin-mcnulty/ Astronomer | LinkedIn https://www.linkedin.com/company/astronomer/ Astronomer | Website https://www.astronomer.io Apache Airflow https://airflow.apache.org/ Prometheus https://prometheus.io/ Splunk https://www.splunk.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 #MachineLearning…

1
Overcoming Data Engineering Challenges at Daiichi Sankyo Europe GmbH with Evgenii Prusov 19:26

לפני 12 ימים19:26

19:26

The shift to a unified data platform is reshaping how pharmaceutical companies manage and orchestrate data. Establishing standards across regions and teams ensures scalability and efficiency in handling large-scale analytics. In this episode, Evgenii Prusov , Senior Data Platform Engineer of Daiichi Sankyo Europe GmbH , joins us to discuss building and scaling a centralized data platform with Airflow and Astronomer. Key Takeaways: 00:00 Introduction. 02:49 Building a centralized data platform for 15 European countries. 05:19 Adopting SaaS to manage Airflow from day one. 07:01 Leveraging Airflow for data orchestration across products. 08:16 Teaching non-Python users how to work with Airflow is challenging. 12:25 Creating a global data community across Europe, the US and Japan. 14:04 Monthly calls help share knowledge and align regional teams. 15:47 Contributing to the open-source Airflow project as a way to deepen expertise. 16:32 Desire for more guidelines, debugging tutorials and testing best practices in Airflow. Resources Mentioned: Evgenii Prusov https://www.linkedin.com/in/prusov/ Daiichi Sankyo Europe GmbH | LinkedIn https://www.linkedin.com/company/daiichi-sankyo-europe-gmbh/ Daiichi Sankyo Europe GmbH | Website https://www.daiichi-sankyo.eu Apache Airflow https://airflow.apache.org/ Astronomer https://www.astronomer.io/ Snowflake https://www.snowflake.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 #MachineLearning…

1
Building a Data-Driven Beauty and Wellness Marketplace at StyleSeat with Paschal Onuorah 23:05

לפני 19 ימים23:05

23:05

StyleSeat is revolutionizing how beauty and wellness professionals grow their businesses through data-driven tools. From streamlining scheduling to optimizing marketing, their platform empowers professionals to focus on their craft while expanding their client base. In this episode, Paschal Onuorah , Senior Data Engineer at StyleSeat , shares how the company leverages Airflow, dbt, and Cosmos to drive marketplace intelligence, improve client connections and deliver measurable growth for professionals. Key Takeaways: 00:00 Introduction. 05:44 The role of the data engineering team in driving business success. 08:52 Leveraging technology for real-time business intelligence. 10:52 Data-driven strategies for improving marketing outcomes. 13:05 How adopting the right tools can increase revenue growth. 14:25 Advantages of simplifying and integrating technical workflows. 18:45 Benefits of multi-environment configurations for development and production. 20:17 Foundational skills and best practices for learning Airflow effectively. 22:33 Opportunities for deeper tool integration and improved data visualization. Resources Mentioned: Paschal Onuorah https://www.linkedin.com/in/onuorah-paschal/ StyleSeat | LinkedIn https://www.linkedin.com/company/styleseat/ StyleSeat | Website https://www.styleseat.com Apache Airflow https://airflow.apache.org/ dbt https://www.getdbt.com/ Astronomer Cosmos https://www.astronomer.io/cosmos/ 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…

1
Building the Future of Airflow Execution at Astronomer with Ian Buss and Piotr Chomiak 22:25

לפני 26 ימים22:25

22:25

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 Buss https://www.linkedin.com/in/ian-buss/ Piotr Chomiak https://www.linkedin.com/in/piotr-chomiak-b1955624/ Astronomer | Website https://www.astronomer.io Apache Airflow https://airflow.apache.org/ Airflow Slack Community https://airflow.apache.org/community/ 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…

1
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לפני 5 weeks24:17

24:17

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…

1
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לפני 6 weeks21:32

21:32

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 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…

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לפני 7 weeks28:02

28:02

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1
Inside Modern Data Infrastructure at Massdriver with Cory O’Daniel and Jake Ferriero 31:24

לפני 8 weeks31:24

31:24

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…

1
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לפני 10 weeks21:55

21:55

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1
Transforming the Airflow UI for Cloudera’s Users with Shubham Raj 22:28

לפני 11 weeks22:28

22:28

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/ 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…

1
Streamlining Thousands of Data Pipelines at Lyft with Yunhao Qing 19:34

לפני 11 weeks19:34

19:34

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/ 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…

1
Transforming Customer Education in Data Engineering at Astronomer with Marc Lamberti 22:19

לפני 13 weeks22:19

22:19

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/ 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…

1
Embracing Data Mesh and SQL Sensors for Scalable Workflows at lastminute.com with Alberto Crespi 30:09

לפני 14 weeks30:09

30:09

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/ 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…

1
The AI-Ready Pipeline: Reimagining Airflow at Veyer® Logistics with Anu Pabla 23:21

לפני 15 weeks23:21

23:21

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/ The ODP Corporation https://www.linkedin.com/company/the-odp-corporation/ The ODP Corporation | Website https://www.theodpcorp.com/homepage Apache Airflow https://airflow.apache.org/ LlamaIndex https://www.llamaindex.ai/ 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/ 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…

1
Streamlining AI and ML Operations at IBM with BJ Adesoji and Ryan Yackel 24:44

לפני 16 weeks24:44

24:44

The orchestration layer is foundational to building robust AI- and ML-powered data pipelines, especially in complex hybrid enterprise environments. IBM’s partnership with Astronomer reflects a strategic alignment to simplify and scale Airflow-based workflows across industries. In this episode, we’re joined by IBM ’s Senior Product Manager, BJ Adesoji , and GTM PM and Growth Leader , Ryan Yackel . We discuss how IBM customers are using Airflow in production, the challenges they face at scale and what the new IBM–Astronomer collaboration unlocks. Key Takeaways: (03:09) The growing importance of orchestration tools in enterprise environments. (04:48) How organizations are expanding orchestration beyond traditional use cases. (05:24) Common patterns across industries adopting orchestration platforms. (07:16) Why orchestration is essential for supporting business-critical workloads. (10:00) The role of orchestration in compliance and regulatory processes. (13:02) Challenges enterprises face when managing orchestration infrastructure. (14:58) Opportunities to simplify and centralize orchestration at scale. (19:11) The value of integrating orchestration with broader data toolchains. (20:54) How AI is shaping the future of orchestrated data workflows. Resources Mentioned: BJ Adesoji https://www.linkedin.com/in/bj-soji/ Ryan Yackel https://www.linkedin.com/in/ryanyackel/ IBM | LinkedIn https://www.linkedin.com/company/databand-ai/ IBM Databand https://www.ibm.com/products/databand IBM DataStage https://www.ibm.com/products/datastage IBM watsonx.governance https://www.ibm.com/products/watsonx-governance IBM Knowledge Catalog https://www.ibm.com/products/knowledge-catalog Apache Airflow https://airflow.apache.org/ watsonx Orchestrate https://www.ibm.com/products/watsonx-orchestrate Domino https://domino.ai/ Astronomer https://www.astronomer.io/ Snowflake https://www.snowflake.com/en/ dbt Labs https://www.getdbt.com/ Amazon SageMaker https://aws.amazon.com/sagemaker/ Cloudera https://www.cloudera.com/ MongoDB https://www.mongodb.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/ 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 Data Flowcast: Mastering Apache Airflow ® for Data Engineering and AI

1
Inside the Custom Framework for Managing Airflow Code at Wix with Gil Reich 31:02

לפני 17 weeks31:02

31:02

Efficient orchestration and maintainability are crucial for data engineering at scale. Gil Reich , Data Developer for Data Science at Wix , shares how his team reduced code duplication, standardized pipelines, and improved Airflow task orchestration using a Python-based framework built within the data science team. In this episode, Gil explains how this internal framework simplifies DAG creation, improves documentation accuracy, and enables consistent task generation for machine learning pipelines. He also shares lessons from complex DAG optimization and maintaining testable code. Key Takeaways: (03:23) Code duplication creates long-term problems. (08:16) Frameworks bring order to complex pipelines. (09:41) Shared functions cut down repetitive code. (17:18) Auto-generated docs stay accurate by design. (22:40) On-demand DAGs support real-time workflows. (25:08) Task-level sensors improve run efficiency. (27:40) Combine local runs with automated tests. (30:09) Clean code helps teams scale faster. Resources Mentioned: Gil Reich https://www.linkedin.com/in/gilreich/ Wix | LinkedIn https://www.linkedin.com/company/wix-com/ Wix | Website https://www.wix.com/ DS DAG Framework https://airflowsummit.org/slides/2024/92-refactoring-dags.pdf Apache Airflow https://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/ 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 Data Flowcast: Mastering Apache Airflow ® for Data Engineering and AI

1
Modernizing Legacy Data Systems With Airflow at Procter & Gamble with Adonis Castillo Cordero 22:13

לפני 18 weeks22:13

22:13

Legacy architecture and AI workloads pose unique challenges at scale, especially in a global enterprise with complex data systems. In this episode, we explore strategies to proactively monitor and optimize pipelines while minimizing downstream failures. Adonis Castillo Cordero , Senior Automation Manager at Procter & Gamble , joins us to share actionable best practices for dependency mapping, anomaly detection and architecture simplification using Apache Airflow. Key Takeaways: (03:13) Integrating legacy data systems into modern architecture. (05:51) Designing workflows for real-time data processing. (07:57) Mapping dependencies early to avoid pipeline failures. (09:02) Building automated monitoring into orchestration frameworks. (12:09) Detecting anomalies to prevent performance bottlenecks. (15:24) Monitoring data quality to catch silent failures. (17:02) Prioritizing responses based on impact severity. (18:55) Simplifying dashboards to highlight critical metrics. Resources Mentioned: Adonis Castillo Cordero https://www.linkedin.com/in/adoniscc/ Procter & Gamble | LinkedIn https://www.linkedin.com/company/procter-and-gamble/ Procter & Gamble | Website http://www.pg.com Apache Airflow https://airflow.apache.org/ OpenLineage https://openlineage.io/ Azure Monitor https://azure.microsoft.com/en-us/products/monitor/ AWS Lookout for Metrics https://aws.amazon.com/lookout-for-metrics/ Monte Carlo https://www.montecarlodata.com/ Great Expectations https://greatexpectations.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/ 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 Data Flowcast: Mastering Apache Airflow ® for Data Engineering and AI

1
Building an End-to-End Data Observability System at Netflix with Joseph Machado 38:54

לפני 19 weeks38:54

38:54

Building reliable data pipelines starts with maintaining strong data quality standards and creating efficient systems for auditing, publishing and monitoring. In this episode, we explore the real-world patterns and best practices for ensuring data pipelines stay accurate, scalable and trustworthy. Joseph Machado , Senior Data Engineer at Netflix , joins us to share practical insights gleaned from supporting Netflix’s Ads business as well as over a decade of experience in the data engineering space. He discusses implementing audit publish patterns, building observability dashboards, defining in-band and separate data quality checks, and optimizing data validation across large-scale systems. Key Takeaways: . (03:14) Supporting data privacy and engineering efficiency within data systems. (10:41) Validating outputs with reconciliation checks to catch transformation issues. (16:06) Applying standardized patterns for auditing, validating and publishing data. (19:28) Capturing historical check results to monitor system health and improvements. (21:29) Treating data quality and availability as separate monitoring concerns. (26:26) Using containerization strategies to streamline pipeline executions. (29:47) Leveraging orchestration platforms for better visibility and retry capability. (31:59) Managing business pressure without sacrificing data quality practices. (35:46) Starting simple with quality checks and evolving toward more complex frameworks. Resources Mentioned: Joseph Machado https://www.linkedin.com/in/josephmachado1991/ Netflix | LinkedIn https://www.linkedin.com/company/netflix/ Netflix | Website https://www.netflix.com/browse Start Data Engineering https://www.startdataengineering.com/ Apache Airflow https://airflow.apache.org/ dbt Labs https://www.getdbt.com/ Great Expectations https://greatexpectations.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/ 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 Data Flowcast: Mastering Apache Airflow ® for Data Engineering and AI

1
Why Developer Experience Shapes Data Pipeline Standards at Next Insurance with Snir Israeli 30:28

לפני 20 weeks30:28

30:28

Creating consistency across data pipelines is critical for scaling engineering teams and ensuring long-term maintainability. In this episode, Snir Israeli , Senior Data Engineer at Next Insurance , shares how enforcing coding standards and investing in developer experience transformed their approach to data engineering. He explains how implementing automated code checks, clear documentation practices and a scoring system helped drive alignment across teams, improve collaboration and reduce technical debt in a fast-growing data environment. Key Takeaways: (02:59) Inconsistencies in code style create challenges for collaboration and maintenance. (04:22) Programmatically enforcing rules helps teams scale their best practices. (08:55) Performance improvements in data pipelines lead to infrastructure cost savings. (13:22) Developer experience is essential for driving adoption of internal tools. (19:44) Dashboards can operationalize standards enforcement and track progress over time. (22:49) Standardization accelerates onboarding and reduces friction in code reviews. (25:39) Linting rules require ongoing maintenance as tools and platforms evolve. (27:47) Starting small and involving the team leads to better adoption and long-term success. Resources Mentioned: Snir Israeli https://www.linkedin.com/in/snir-israeli/ Next Insurance | LinkedIn https://www.linkedin.com/company/nextinsurance/ Next Insurance | Website https://www.nextinsurance.com/ Apache Airflow https://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/ 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 Data Flowcast: Mastering Apache Airflow ® for Data Engineering and AI

1
Data Quality and Observability at Tekmetric with Ipsa Trivedi 22:49

לפני 21 weeks22:49

22:49

Airflow’s adaptability is driving Tekmetric’s ability to unify complex data workflows, deliver accurate insights and support both internal operations and customer-facing services — all within a rapidly growing startup environment. In this episode, Ipsa Trivedi , Lead Data Engineer at Tekmetric , shares how her team is standardizing pipelines while supporting unique customer needs. She explains how Airflow enables end-to-end data services, simplifies orchestration across varied sources and supports scalable customization. Ipsa also highlights early wins with Airflow, its intuitive UI and the team's roadmap toward data quality, observability and a future self-serve data platform. Key Takeaways: (02:26) Powering auto shops nationwide with a unified platform. (05:17) A new data team was formed to centralize and scale insights. (07:23) Flexible, open source and made to fit — Airflow wins. (10:42) Pipelines handle anything from email to AWS. (12:15) Custom DAGs fit every team’s unique needs. (17:01) Data quality checks are built into the plan. (18:17) Self-serve data mesh is the end goal. (19:59) Airflow now fits so well, there's nothing left on the wishlist. Resources Mentioned: Ipsa Trivedi https://www.linkedin.com/in/ipsatrivedi/ Tekmetric | LinkedIn https://www.linkedin.com/company/tekmetric/ Tekmetric | Website https://www.tekmetric.com/ Apache Airflow https://airflow.apache.org/ AWS RDS https://aws.amazon.com/free/database/?trk=fc551e06-56b0-418c-9ddd-5c9dba18569b&sc_channel=ps&ef_id=CjwKCAjwzMi_BhACEiwAX4YZULS4jV2Xpnpcac_Q3eS9BAg-klKUDyCt6XSdOul8BLHkmWzFFh4NXRoCGhQQAvD_BwE:G:s&s_kwcid=AL!4422!3!548989592596!e!!g!!amazon%20sql%20database!11543056228!112002958549&gclid=CjwKCAjwzMi_BhACEiwAX4YZULS4jV2Xpnpcac_Q3eS9BAg-klKUDyCt6XSdOul8BLHkmWzFFh4NXRoCGhQQAvD_BwE Astro by Astronomer https://www.astronomer.io/product/ 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/ 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 Data Flowcast: Mastering Apache Airflow ® for Data Engineering and AI

1
Introducing Apache Airflow® 3 with Vikram Koka and Jed Cunningham 27:28

לפני 22 weeks27:28

27:28

The Airflow 3.0 release marks a significant leap forward in modern data orchestration, introducing architectural upgrades that improve scalability, flexibility and long-term maintainability. In this episode, we welcome Vikram Koka , Chief Strategy Officer at Astronomer , and Jed Cunningham , Principal Software Engineer at Astronomer , to discuss the architectural foundations, new features and future implications of this milestone release. They unpack the rationale behind DAG versioning and task execution interface, explain how Airflow now integrates more seamlessly within broader data ecosystems and share how these changes lay the groundwork for multi-cloud deployments, language-agnostic workflows and stronger enterprise security. Key Takeaways: (02:28) Modern orchestration demands new infrastructure approaches. (05:02) Removing legacy components strengthens system stability. (06:26) Major releases provide the opportunity to reduce technical debt. (08:31) Frontend and API modernization enable long-term adaptability. (09:36) Event-based triggers expand integration possibilities. (11:54) Version control improves visibility and execution reliability. (14:57) Centralized access to workflow definitions increases flexibility. (21:49) Decoupled architecture supports distributed and secure deployments. (26:17) Community collaboration is essential for sustainable growth. Resources Mentioned: Astronomer Website https://www.astronomer.io Apache Airflow https://airflow.apache.org/ Git Bundle https://git-scm.com/book/en/v2/Git-Tools-Bundling FastAPI https://fastapi.tiangolo.com/ React https://react.dev/ 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/ 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 Data Flowcast: Mastering Apache Airflow ® for Data Engineering and AI

1
Airflow in Action: Powering Instacart's Complex Ecosystem 25:14

לפני 23 weeks25:14

25:14

The evolution of data orchestration at Instacart highlights the journey from fragmented systems to robust, standardized infrastructure. This transformation has enabled scalability, reliability and democratization of tools for diverse user personas. In this episode, we’re joined by Anant Agarwal , Software Engineer at Instacart , who shares insights into Instacart's Airflow journey, from its early adoption in 2019 to the present-day centralized cluster approach. Anant discusses the challenges of managing disparate clusters, the implementation of remote executors, and the strategic standardization of infrastructure and DAG patterns to streamline workflows. Key Takeaways: (03:49) The impact of external events on business growth and technological evolution. (04:31) Challenges of managing decentralized systems across multiple teams. (06:14) The importance of standardizing infrastructure and processes for scalability. (09:51) Strategies for implementing efficient and repeatable deployment practices. (12:17) Addressing diverse user personas with tailored solutions. (14:47) Leveraging remote execution to enhance flexibility and scalability. (18:36) Benefits of transitioning to a centralized system for organization-wide use. (20:57) Maintaining an upgrade cadence to stay aligned with the latest advancements. (23:35) Anticipation for new features and improvements in upcoming software versions. Resources Mentioned: Anant Agarwal https://www.linkedin.com/in/anantag/ Instacart | LinkedIn https://www.linkedin.com/company/instacart/ Instacart | Website https://www.instacart.com Apache Airflow https://airflow.apache.org/ AWS Amazon https://aws.amazon.com/ecs/ Terraform https://www.terraform.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/ Thanks for listening to “The Data Flowcast: Mastering Airflow for Data Engineering & 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 Data Flowcast: Mastering Apache Airflow ® for Data Engineering and AI

1
From ETL to Airflow: Transforming Data Engineering at Deloitte Digital with Raviteja Tholupunoori 27:42

לפני 24 weeks27:42

27:42

Data orchestration at scale presents unique challenges, especially when aiming for flexibility and efficiency across cloud environments. Choosing the right tools and frameworks can make all the difference. In this episode, Raviteja Tholupunoori, Senior Engineer at Deloitte Digital , joins us to explore how Airflow enhances orchestration, scalability and cost efficiency in enterprise data workflows. Key Takeaways: (01:45) Early challenges in data orchestration before implementing Airflow. (02:42) Comparing Airflow with ETL tools like Talend and why flexibility matters. (04:24) The role of Airflow in enabling cloud-agnostic data processing. (05:45) Key lessons from managing dynamic DAGs at scale. (13:15) How hybrid executors improve performance and efficiency. (14:13) Best practices for testing and monitoring workflows with Airflow. (15:13) The importance of mocking mechanisms when testing DAGs. (17:57) How Prometheus, Grafana and Loki support Airflow monitoring. (22:03) Cost considerations when running Airflow on self-managed infrastructure. (23:14) Airflow’s latest features, including hybrid executors and dark mode. Resources Mentioned: Raviteja Tholupunoori https://www.linkedin.com/in/raviteja0096/?originalSubdomain=in Deloitte Digital https://www.linkedin.com/company/deloitte-digital/ Apache Airflow https://airflow.apache.org/ Grafana https://grafana.com/solutions/apache-airflow/monitor/ Astronomer Presents: Exploring Apache Airflow® 3 Roadshows https://www.astronomer.io/events/roadshow/ 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/ Thanks for listening to “The Data Flowcast: Mastering Airflow for Data Engineering & 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 Data Flowcast: Mastering Apache Airflow ® for Data Engineering and AI

1
A Deep Dive Into the 2025 State of Airflow Survey Results with Tamara Fingerlin of Astronomer 23:26

לפני 25 weeks23:26

23:26

The 2025 State of Airflow report sheds light on how global users are adopting, evolving and innovating with Apache Airflow. With over 5,000 responses from 116 countries, the survey reveals critical insights into Airflows’ role in business operations, new use cases and what’s ahead for the community. In this episode, Tamara Fingerlin , Developer Advocate at Astronomer , walks us through her process of analyzing survey data, key trends from the report and what to expect from Airflow 3.0. Key Takeaways: (02:14) The State of Airflow report combines anonymized telemetry and survey results. (03:25) The survey received thousands of responses from many countries, showcasing global reach. (04:49) The survey process involves multiple steps, from question selection to report creation. (09:00) Many users expect to increase Airflow usage for revenue-generating or external use cases. (11:04) Experienced users tend to utilize Airflow more for advanced use cases like MLOps. (15:13) UI improvements offer enhanced navigation and error visibility. (18:15) Architectural changes enable new capabilities like remote execution and language support. (19:40) Long-requested features will be available in the new major release. (21:00) Future aspirations include integrating data visualization capabilities into the UI. Resources Mentioned: Tamara Fingerlin https://www.linkedin.com/in/tamara-janina-fingerlin/ Astronomer | LinkedIn https://www.linkedin.com/company/astronomer/ Astronomer | Website https://www.astronomer.io Apache Airflow https://airflow.apache.org/ 2025 State of Airflow Webinar https://www.astronomer.io/airflow/state-of-airflow/ Airflow Slack https://apache-airflow-slack.herokuapp.com/ Astronomer Presents: Exploring Apache Airflow® 3 Roadshows https://www.astronomer.io/events/roadshow/ 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/ Thanks for listening to “The Data Flowcast: Mastering Airflow for Data Engineering & 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 Data Flowcast: Mastering Apache Airflow ® for Data Engineering and AI

1
The Software Risk That Affects Everyone and How To Address It with Michael Winser and Jarek Potiuk 28:27

לפני 27 weeks28:27

28:27

The security of open-source software is a growing concern, especially as dependencies and regulations become more complex, making it essential to understand how to manage software supply chains effectively. In this episode, we sit down with Michael Winser , Co-Founder at Alpha-Omega and Security Strategy Ambassador at Eclipse Foundation , and Jarek Potiuk , Member of the Security Committee at the Apache Software Foundation , to discuss the challenges of securing Airflow’s dependencies, the evolving landscape of open-source security and how contributors can help strengthen the ecosystem. Key Takeaways: (02:43) Jarek quit his full-time engineer position and uses Airflow as a freelancer. (04:32) Michael finds happiness in having meaningful work with open-source security. (07:01) Software supply chain security focuses on correctness, integrity and availability. (08:44) Airflow’s 790 dependencies present a unique security challenge. (09:43) Airflow’s security team has significantly improved its vulnerability response. (10:22) The transition to Airflow 3 emphasizes enterprise security readiness. (16:20) The ‘Three Fs’ approach: fix it, fork it, or forget it. (18:45) Dependency health is often more critical than fixing known vulnerabilities. (23:32) The ‘Three Fs’ in action. (26:26) Open-source contributors play a key role in supply chain security. Resources Mentioned: Michael Winser - https://www.linkedin.com/in/michaelw/ Jarek Potiuk - https://www.linkedin.com/in/jarekpotiuk/ Apache Airflow - https://airflow.apache.org/ Apache Software Foundation | LinkedIn - https://www.linkedin.com/company/the-apache-software-foundation/ Apache Software Foundation | Website - https://www.apache.org/ Eclipse Foundation | LinkedIn - https://www.linkedin.com/company/eclipse-foundation/ Eclipse Foundation | Website - https://www.eclipse.org/org/foundation/ OpenSSF Working Groups - https://openssf.org/community/openssf-working-groups/ Astronomer Roadshow: Exploring Apache Airflow 3 | London https://www.astronomer.io/events/roadshow/london/ Astronomer Roadshow: Exploring Apache Airflow 3 | New York https://www.astronomer.io/events/roadshow/new-york/ Astronomer Roadshow: Exploring Apache Airflow 3 | Sydney https://www.astronomer.io/events/roadshow/sydney/ Astronomer Roadshow: Exploring Apache Airflow 3 | San Francisco https://www.astronomer.io/events/roadshow/san-francisco/ Astronomer Roadshow: Exploring Apache Airflow 3 | Chicago https://www.astronomer.io/events/roadshow/chicago/ Thanks for listening to “The Data Flowcast: Mastering Airflow for Data Engineering & 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 Data Flowcast: Mastering Apache Airflow ® for Data Engineering and AI

1
Building Scalable ML Infrastructure at Outerbounds with Savin Goyal 36:46

לפני 28 weeks36:46

36:46

Machine learning is changing fast, and companies need better tools to handle AI workloads. The right infrastructure helps data scientists focus on solving problems instead of managing complex systems. In this episode, we talk with Savin Goyal , Co-Founder and CTO at Outerbounds , about building ML infrastructure, how orchestration makes workflows easier and how Metaflow and Airflow work together to simplify data science. Key Takeaways: (02:02) Savin spent years building AI and ML infrastructure, including at Netflix. (04:05) ML engineering was not a defined role a decade ago. (08:17) Modernizing AI and ML requires balancing new tools with existing strengths. (10:28) ML workloads can be long-running or require heavy computation. (15:29) Different teams at Netflix used multiple orchestration systems for specific needs. (20:10) Stable APIs prevent rework and keep projects moving. (21:07) Metaflow simplifies ML workflows by optimizing data and compute interactions. (25:53) Limited local computing power makes running ML workloads challenging. (27:43) Airflow UI monitors pipelines, while Metaflow UI gives ML insights. (33:13) The most successful data professionals focus on business impact, not just technology. Resources Mentioned: Savin Goyal - https://www.linkedin.com/in/savingoyal/ Outerbounds - https://www.linkedin.com/company/outerbounds/ Apache Airflow - https://airflow.apache.org/ Metaflow - https://metaflow.org/ Netflix’s Maestro Orchestration System - https://netflixtechblog.com/maestro-netflixs-workflow-orchestrator-ee13a06f9c78?gi=8e6a067a92e9#:~:text=Maestro%20is%20a%20fully%20managed,data%20between%20different%20storages%2C%20etc. TensorFlow - https://www.tensorflow.org/ PyTorch - https://pytorch.org/ Thanks for listening to “The Data Flowcast: Mastering Airflow for Data Engineering & 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 Data Flowcast: Mastering Apache Airflow ® for Data Engineering and AI

1
Customizing Airflow for Complex Data Environments at Stripe with Nick Bilozerov and Sharadh Krishnamurthy 27:40

לפני 29 weeks27:40

27:40

Keeping data pipelines reliable at scale requires more than just the right tools — it demands constant innovation. In this episode, Nick Bilozerov , Senior Data Engineer at Stripe , and Sharadh Krishnamurthy , Engineering Manager at Stripe, discuss how Stripe customizes Airflow for its needs, the evolution of its data orchestration framework and the transition to Airflow 2. They also share insights on scaling data workflows while maintaining performance, reliability and developer experience. Key Takeaways: (02:04) Stripe’s mission is to grow the GDP of the internet by supporting businesses with payments and data. (05:08) 80% of Stripe engineers use data orchestration, making scalability critical. (06:06) Airflow powers business reports, regulatory needs and ML workflows. (08:02) Custom task frameworks improve dependencies and validation. (08:50) "User scope mode" enables local testing without production impact. (10:39) Migrating to Airflow 2 improves isolation, safety and scalability. (16:40) Monolithic DAGs caused database issues, prompting a service-based shift. (19:24) Frequent Airflow upgrades ensure stability and access to new features. (21:38) DAG versioning and backfill improvements enhance developer experience. (23:38) Greater UI customization would offer more flexibility. Resources Mentioned: Nick Bilozerov - https://www.linkedin.com/in/nick-bilozerov/ Sharadh Krishnamurthy - https://www.linkedin.com/in/sharadhk/ Apache Airflow - https://airflow.apache.org/ Stripe | LinkedIn - https://www.linkedin.com/company/stripe/ Stripe | Website - https://stripe.com/ Thanks for listening to “The Data Flowcast: Mastering Airflow for Data Engineering & 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 Data Flowcast: Mastering Apache Airflow ® for Data Engineering and AI

1
Harnessing Airflow for Data-Driven Policy Research at CSET with Jennifer Melot 17:54

לפני 30 weeks17:54

17:54

Turning complex datasets into meaningful analysis requires robust data infrastructure and seamless orchestration. In this episode, we’re joined by Jennifer Melot , Technical Lead at the Center for Security and Emerging Technology (CSET) at Georgetown University, to explore how Airflow powers data-driven insights in technology policy research. Jennifer shares how her team automates workflows to support analysts in navigating complex datasets. Key Takeaways: (02:04) CSET provides data-driven analysis to inform government decision-makers. (03:54) ETL pipelines merge multiple data sources for more comprehensive insights. (04:20) Airflow is central to automating and streamlining large-scale data ingestion. (05:11) Larger-scale databases create challenges that require scalable solutions. (07:20) Dynamic DAG generation simplifies Airflow adoption for non-engineers. (12:13) DAG Factory and dynamic task mapping can improve workflow efficiency. (15:46) Tracking data lineage helps teams understand dependencies across DAGs. (16:14) New Airflow features enhance visibility and debugging for complex pipelines. Resources Mentioned: Jennifer Melot - https://www.linkedin.com/in/jennifer-melot-aa710144/ Center for Security and Emerging Technology (CSET) - https://www.linkedin.com/company/georgetown-cset/ Apache Airflow - https://airflow.apache.org/ Zenodo - https://zenodo.org/ OpenLineage - https://openlineage.io/ Cloud Dataplex - https://cloud.google.com/dataplex Thanks for listening to “The Data Flowcast: Mastering Airflow for Data Engineering & 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 Data Flowcast: Mastering Apache Airflow ® for Data Engineering and AI

1
Hybrid Testing Solutions for Autonomous Driving at Bosch with Jens Scheffler and Christian Schilling 33:45

לפני 32 weeks33:45

33:45

Testing autonomous vehicles demands precision, scalability and powerful orchestration tools — enter Apache Airflow, a key component of Bosch’s cutting-edge testing framework. In this episode, we sit down with Jens Scheffler , Test Execution Cluster Technical Architect, and Christian Schilling , Product Owner Open Loop Testing Automated Driving, both at Bosch , to explore how Bosch harnesses Airflow to streamline complex testing scenarios. They share insights on scaling workflows, integrating hybrid infrastructures and ensuring vehicle safety through rigorous automated testing. Key Takeaways: (01:35) Airflow orchestrates millions of test hours for autonomous systems. (03:15) Jens scales distributed systems with Kubernetes for job orchestration. (06:02) Airflow runs hundreds of tests simultaneously. (06:44) Virtual testing reduces costs and on-road trials. (12:19) Unified APIs and GUIs streamline operations. (15:05) Self-service setups empower Bosch teams. (18:00) Physical hardware integration ensures real-world timing. (20:30) Dynamic task mapping scales workflows efficiently. (25:22) Open-source contributions improve stability. (31:06) Edge and Celery executors power Bosch's hybrid scheduling. Resources Mentioned: Jens Scheffler - https://www.linkedin.com/in/jens-scheffler/ Christian Schilling - https://www.linkedin.com/in/christian-schilling-a5078831a/ Bosch - https://www.linkedin.com/company/bosch/ Apache Airflow - https://airflow.apache.org/ Kubernetes - https://kubernetes.io GitHub - https://github.com Edge Executor - https://airflow.apache.org/docs/apache-airflow/stable/core-concepts/executor/index.html Thanks for listening to “The Data Flowcast: Mastering Airflow for Data Engineering & 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 Data Flowcast: Mastering Apache Airflow ® for Data Engineering and AI

1
Overcoming Airflow Scaling Challenges at Monzo Bank with Jonathan Rainer 43:39

לפני 33 weeks43:39

43:39

Scaling a data orchestration platform to manage thousands of tasks daily demands innovative solutions and strategic problem-solving. In this episode, we explore the complexities of scaling Airflow and the challenges of orchestrating thousands of tasks in dynamic data environments. Jonathan Rainer , Former Platform Engineer at Monzo Bank , joins us to share his journey optimizing data pipelines, overcoming UI limitations and ensuring DAG consistency in high-stakes scenarios. Key Takeaways: (03:11) Using Airflow to schedule computation in BigQuery. (07:02) How DAGs with 8,000+ tasks were managed nightly. (08:18) Ensuring accuracy in regulatory reporting for banking. (11:35) Handling task inconsistency and DAG failures with automation. (16:09) Building a service to resolve DAG consistency issues in Airflow. (25:05) Challenges with scaling the Airflow UI for thousands of tasks. (27:03) The role of upstream and downstream task management in Airflow. (37:33) The importance of operational metrics for monitoring Airflow health. (39:19) Balancing new tools with root cause analysis to address scaling issues. (41:35) Why scaling solutions require both technical and leadership buy-in Resources Mentioned: Jonathan Rainer - https://www.linkedin.com/in/jonathan-rainer/ Monzo Bank - https://www.linkedin.com/company/monzo-bank/ Apache Airflow - https://airflow.apache.org/ BigQuery - https://airflow.apache.org/docs/apache-airflow-providers-google/stable/operators/cloud/bigquery.html Kubernetes - https://kubernetes.io/ Thanks for listening to “The Data Flowcast: Mastering Airflow for Data Engineering & 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 Data Flowcast: Mastering Apache Airflow ® for Data Engineering and AI

1
Airflow Breeze 46:57

לפני 6 years46:57

46:57

This week, we had the pleasure of meeting up with Jarek Potiuk, Principal Software Engineer at Polidea and Apache Airflow committer, to discuss his most recent contribution to the community, Airflow Breeze. Jarek deeply values developer productivity and realized while building a team of Airflow committers that, in order to open a PR on the project, passing unit tests and waiting for the CI build was a cumbersome process that could take up to a few hours. Breeze seeks to improve that experience for Airflow committers and lower the barrier-to-entry of contribution for folks that are new to the open-source community. You can read more about Airflow Breeze here: https://www.polidea.com/blog/its-a-breeze-to-develop-apache-airflow/#the-apache-airflow-projects-setup…

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

1
Open Source and Airflow at Google 39:03

לפני 6 years39:03

39:03

This episode kicks off season 2 of The Airflow Podcast. In this next season, we'll focus on the future of Airflow and chat with leading members of the community to paint a picture of what's to come. We're pumped to be diving back into this project and look forward to the great conversations we have lined up. This week, we chatted with James Malone, Product Manager of Google's Cloud Composer. James had some interesting things to say about open source at Google and where his team plans on contributing most to the project going forward. As always, thanks for listening and please email pete@astronomer.io if you have any feedback or would like to be considered as a guest.…

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

1
Airflow 1.10 Release 29:03

לפני 7 years29:03

29:03

This week, we met up with Ash Berlin-Taylor to discuss the recent 1.10 release, what it's like to be a release manager for an open source project, Airflow's bid to graduate from incubating status, and the next phase of Airflow project development. As mentioned in our podcast intro, we at Astronomer are hiring Data Engineers who are passionate about contributing to open source and making Airflow great. Please shoot us an email at humans@astronomer.io if you're interested in hearing more about the fully-remote opportunity. Check us out at www.astronomer.io if you're interested in seeing a demo of our platform.…

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

1
Role-Based Access Control (RBAC) 37:28

לפני 7 years37:28

37:28

This time, we met up with WePay's Joy Gao to talk through her work on the RBAC components in the recent Airflow 1.10 release. We dove deep into what inspired her work and took some time to discuss what it's like to be a woman contributing to a predominately male open-source community. Hope you enjoy! If you'd like to get started using Airflow in your org, check out our recently-launched Spacecamp program here: https://www.astronomer.io/spacecamp Feel free to email me at pete@astronomer.io with any feedback or if you'd like to be considered as a guest!…

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

1
Airflow on Kubernetes 55:11

לפני 7 years55:11

55:11

In this episode, we dove into the relationship between Airflow and Kuberenetes and interviewed Daniel Imberman, Senior Software Engineer at Bloomberg (1:30), and Greg Neiheisel, CTO here at Astronomer (37:31). Daniel has done most of the work on the Kubernetes executor for Airflow and Greg plans to take on a chunk of the development going forward, so it was really interesting to hear both of their perspectives on the project. Enjoy!…

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

1
Pain Points 38:57

לפני 7 years38:57

38:57

This week, we’ll examine conversations with both old guests and new to paint a comprehensive picture of Airflow’s pain points. While we still undoubtedly believe that Airflow is the future of ETL, it’s important to acknowledge that any incubating project will have issues, and bringing those issues to the forefront of the community’s attention will help shape the future of the project. We’ll talk with Thomas La Piana (1:36), Data Engineer at OrderMyGear, Frank Hsu (14:20), Data Engineer at mines.io, and Alan Cruickshank (27:41), business insights and data manager at tails.com. Check out our open-source library of Airflow plugins at github.com/airflow-plugins, and feel free to contribute anything that you've been working on! If you're interested in being on the podcast or have any feedback on how you think we could make it better, shoot me an email at pete@astronomer.io…

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

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Competitors: Luigi 27:39

לפני 7 years27:39

27:39

On this episode, we linked up with Erik Bernhardsson (@erikbern), creator of Luigi and CTO of Better Mortgage. We chatted about everything from the motivations behind Luigi's creation and his current thoughts on Airflow- we hope you enjoy! Check out: - Erik's blog at erikbern.com - Our open-source library of Airflow plugins at github.com/airflow-plugins All podcast feedback is hugely appreciated- feel free to email me at pete@astronomer.io if you have any thoughts.…

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

1
Best Practices 58:31

לפני 8 years58:31

58:31

In this episode, we dive into Airflow Best Practices and include longer portions of interviews with Alan Cruickshank (1:30), Business Insights and Data Manager at Tails.com, Chris Riccomini (7:27), Principal Software Engineer at WePay, and Bolke de Bruin(31:45), Head of Advanced Analytics Technology at ING. Hope you enjoy! We're still working to get better at podcasting, so please send over any feedback to pete@astronomer.io. We really appreciate hearing what the community has to say, and your feedback is hugely helpful in making us better. If you're interested in Astronomer Spacecamp, a guided Airflow development course, you can find more info on that here: https://www.astronomer.io/blog/announcing-astronomer-spacecamp/ We also launched our Managed Airflow on Product Hunt last week- you can check that out here: https://www.producthunt.com/posts/apache-airflow-on-astronomer Thanks so much for listening!…

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

1
Use Cases 1:15:47

לפני 8 years1:15:47

1:15:47

Episode 2 of The Airflow Podcast is here to discuss six specific use cases that we’ve seen for Apache Airflow. Here’s the lineup: Patrick Atwater (@patwater), Water Data Projects Manager at ARGO Labs: 2:03-5:35 Maksime Pecherskiy (@mrmaksimize), CDO of San Diego: 5:35-23:06 Scott Halgrim (@shalgrim), Data Engineer at Zapier: 23:06-27:27 Bolke de Bruin (@bolke2028), Head of Advanced Analytics at ING: 27:27-39:46 Chris Riccomini (@criccomini), Principal Software Engineer at WePay: 39:46-54:20 Ben Gregory (@benbeingbin), Data Engineer (and noted craft soda enthusiast) at Astronomer: 54:20-1:14:38 Contribute to our open-source library of Airflow plugins at github.com/airflow-plugins Contact us at www.astronomer.io if you’re interested in Spacecamp: A guided development program to get your team up and running on Airflow.…

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

1
The Origins of Airflow 45:09

לפני 8 years45:09

45:09

For the first episode of the Airflow Podcast, we met up with Maxime Beauchemin, creator of Airflow, to explore the motivations behind its creation and the problems it was designed to solve. We asked Maxime for his definition of Airflow, the design principles behind hook/operator use, and his vision for the project. Speaker list: Pete DeJoy - Product at Astronomer Viraj Parekh - Data Engineer at Astronomer Maxime Beauchemin - Software Engineer at Lyft, creator of Airflow Talk mentioned at the end of the podcast- Advanced Data Engineering Patterns with Apache Airflow: http://www.ustream.tv/recorded/109227704 Maxime's Blog: https://medium.com/@maximebeauchemin…

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

1
Season One Teaser 3:02

לפני 8 years3:02

3:02

A sneak peek at our upcoming podcast about Apache Airflow. Featured in this clip (in order of appearance): Pete DeJoy - Product Specialist at Astronomer Patrick Atwater - Water Data Projects Manager at ARGO Labs Maksime Pecherskiy - Chief Data Officer of the City of San Diego Bolke de Bruin - Head of Advanced Analytics at ING…

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Inside Airflow 3: Redefining Data Engineering with Vikram Koka