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

• Transforming Customer Education in Data Engineering at Astronomer with Marc Lamberti

  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 Lambertihttps://www.linkedin.com/in/marclamberti/Astronomer | LinkedInhttps://www.linkedin.com/company/astronomer/Astronomer Academyhttps://academy.astronomer.io/Airflow Fundamentals Certificationhttps://www.astronomer.io/certification/DAG Authoring Certificationhttps://academy.astronomer.io/plan/astronomer-certification-dag-authoring-for-apache-airflow-examThe Complete Hands-On Introduction to Airflowhttps://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=PMNVD3025https://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

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• Embracing Data Mesh and SQL Sensors for Scalable Workflows at lastminute.com with Alberto Crespi

  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 Crespihttps://www.linkedin.com/in/crespialberto/lastminute.com | Websitehttps://lastminute.comApache Airflowhttps://airflow.apache.org/dbt Labshttps://www.getdbt.com/Astronomer Cosmoshttps://github.com/astronomer/astronomer-cosmosGitLabSlackhttps://slack.com/Kuberneteshttps://kubernetes.io/Confluencehttps://www.atlassian.com/software/confluenceSlackhttps://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

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• The AI-Ready Pipeline: Reimagining Airflow at Veyer® Logistics with Anu Pabla

  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 Pablahttps://www.linkedin.com/in/atomicap/The ODP Corporationhttps://www.linkedin.com/company/the-odp-corporation/The ODP Corporation | Websitehttps://www.theodpcorp.com/homepageApache Airflowhttps://airflow.apache.org/LlamaIndexhttps://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

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• Streamlining AI and ML Operations at IBM with BJ Adesoji and Ryan Yackel

  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 Adesojihttps://www.linkedin.com/in/bj-soji/Ryan Yackelhttps://www.linkedin.com/in/ryanyackel/IBM | LinkedInhttps://www.linkedin.com/company/databand-ai/IBM Databandhttps://www.ibm.com/products/databandIBM DataStagehttps://www.ibm.com/products/datastageIBM watsonx.governancehttps://www.ibm.com/products/watsonx-governanceIBM Knowledge Cataloghttps://www.ibm.com/products/knowledge-catalogApache Airflowhttps://airflow.apache.org/watsonx Orchestratehttps://www.ibm.com/products/watsonx-orchestrateDominohttps://domino.ai/Astronomerhttps://www.astronomer.io/Snowflakehttps://www.snowflake.com/en/dbt Labshttps://www.getdbt.com/Amazon SageMakerhttps://aws.amazon.com/sagemaker/Clouderahttps://www.cloudera.com/MongoDBhttps://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

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• Inside the Custom Framework for Managing Airflow Code at Wix with Gil Reich

  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 Reichhttps://www.linkedin.com/in/gilreich/Wix | LinkedInhttps://www.linkedin.com/company/wix-com/Wix | Websitehttps://www.wix.com/DS DAG Frameworkhttps://airflowsummit.org/slides/2024/92-refactoring-dags.pdfApache Airflowhttps://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

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