The Data Flowcast: Mastering Apache Airflow ® For Data Engineering And AI podcast

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

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Welcome to The Data Flowcast: Mastering Apache Airflow ® for Data Engineering and AI— the podcast where we keep you up to date with insights and ideas propelling the Airflow community forward. Join us each week, as we explore the current state, future and potential of Airflow with leading thinkers in the community, and discover how best to leverage this workflow management system to meet the ever-evolving needs of data engineering and AI ecosystems. Podcast Webpage: https://www.astronomer.io/podcast/

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#IT Management #Tech #Industries #Airflow

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

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updated לפני 5 ימים

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53 פרקים

#IT Management #Tech #Industries #Airflow

כל הפרקים

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

לפני 5 ימים27:28

הפעל מאוחר יותר

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לייק

אהבתי

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…

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

לפני 12 ימים25:14

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…

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

לפני 19 ימים27:42

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…

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

לפני 26 ימים23:26

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…

1
Airflow’s Role in the Rise of DataOps with Andy Byron 26:15

לפני 5 weeks26:15

26:15

The orchestration layer is evolving into a critical component of the modern data stack. Understanding its role in DataOps is key to optimizing workflows, improving reliability and reducing complexity. In this episode, Andy Byron , CEO at Astronomer , discusses the rapid growth of Apache Airflow, the increasing importance of orchestration and how Astronomer is shaping the future of DataOps. Key Takeaways: (01:54) Orchestration is central to modern data workflows. (03:16) Airflow 3.0 will enhance usability and flexibility. (05:14) AI-driven workloads demand zero-downtime orchestration. (08:13) DataOps relies on orchestration for seamless operations. (11:05) Integration across ingestion, transformation and governance is key. (17:24) The future of DataOps is consolidation and automation. (19:13) Enterprises use Airflow to process massive data volumes. (23:20) Product innovation is driven by customer needs and feedback. Resources Mentioned: Andy Byron https://www.linkedin.com/in/andy-byron-417a429/ Astronomer | LinkedIn https://www.linkedin.com/company/astronomer/ Astronomer | Website https://www.astronomer.io Apache Airflow https://airflow.apache.org/ State of Airflow Webinar https://www.astronomer.io/events/webinars/the-state-of-airflow-2025-video/ Astronomer Observe https://www.astronomer.io/product/observe/ 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…

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

לפני 6 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…

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

לפני 7 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…

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

לפני 8 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…

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

לפני 9 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…

1
Leveraging Airflow To Build Scalable and Reliable Data Platforms at 99acres.com with Samyak Jain 25:08

לפני 10 weeks25:08

25:08

Data orchestration is evolving rapidly, with dynamic workflows becoming the cornerstone of modern data engineering. In this episode, we are joined by Samyak Jain , Senior Software Engineer - Big Data at 99acres.com . Samyak shares insights from his journey with Apache Airflow, exploring how his team built a self-service platform that enables non-technical teams to launch data pipelines and marketing campaigns seamlessly. Key Takeaways: (02:02) Starting a career in data engineering by troubleshooting Airflow pipelines. (04:27) Building self-service portals with Airflow as the backend engine. (05:34) Utilizing API endpoints to trigger dynamic DAGs with parameterized templates. (09:31) Managing a dynamic environment with over 1,400 active DAGs. (11:14) Implementing fault tolerance by segmenting data workflows into distinct layers. (14:15) Tracking and optimizing query costs in AWS Athena to save $7K monthly. (16:22) Automating cost monitoring with real-time alerts for high-cost queries. (17:15) Streamlining Airflow metadata cleanup to prevent performance bottlenecks. (21:30) Efficiently handling one-time and recurring marketing campaigns using Airflow. (24:18) Advocating for Airflow features that improve resource management and ownership tracking. Resources Mentioned: Samyak Jain - https://www.linkedin.com/in/samyak-jain-ab5830169/ 99acres.com - https://www.linkedin.com/company/99acres/ Apache Airflow - https://airflow.apache.org/ AWS Athena - https://aws.amazon.com/athena/ Kafka - https://kafka.apache.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…

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

לפני 11 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…

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

לפני 12 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…

1
Orchestrating Analytics and AI Workflows at Telia with Arjun Anandkumar 26:00

לפני 13 weeks26:00

26:00

T he future of data engineering lies in seamless orchestration and automation. In this episode, Arjun Anandkumar , Data Engineer at Telia , shares how his team uses Airflow to drive analytics and AI workflows. He highlights the challenges of scaling data platforms and how adopting best practices can simplify complex processes for teams across the organization. Arjun also discusses the transformative role of tools like Cosmos and Terraform in enhancing efficiency and collaboration. Key Takeaways: (02:16) Telia operates across the Nordics and Baltics, focusing on telecom and energy services. (03:45) Airflow runs dbt models seamlessly with Cosmos on AWS MWAA. (05:47) Cosmos improves visibility and orchestration in Airflow. (07:00) Medallion Architecture organizes data into bronze, silver and gold layers. (08:34) Task group challenges highlight the need for adaptable workflows. (15:04) Scaling managed services requires trial, error and tailored tweaks. (19:46) Terraform scales infrastructure, while YAML templates manage DAGs efficiently. (20:00) Templated DAGs and robust testing enhance platform management. (24:15) Open-source resources drive innovation in Airflow practices. Resources Mentioned: Arjun Anandkumar - https://www.linkedin.com/in/arjunanand1/?originalSubdomain=dk Telia - https://www.linkedin.com/company/teliacompany/ Apache Airflow - https://airflow.apache.org/ Cosmos by Astronomer - https://www.astronomer.io/cosmos/ Terraform - https://www.terraform.io/ Medallion Architecture by Databricks - https://www.databricks.com/glossary/medallion-architecture 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…

1
The Role of Airflow in Finance Transformation at Etraveli Group with Mihir Samant 21:19

לפני 14 weeks21:19

21:19

Transforming bottlenecked finance processes into streamlined, automated systems requires the right tools and a forward-thinking approach. In this episode, Mihir Samant , Senior Data Analyst at Etraveli Group , joins us to share how his team leverages Airflow to revolutionize finance automation. With extensive experience in data workflows and a passion for open-source tools, Mihir provides valuable insights into building efficient, scalable systems. We explore the transformative power of Airflow in automating workflows and enhancing data orchestration within the finance domain. Key Takeaways: (02:14) Etraveli Group specializes in selling affordable flight tickets and ancillary services. (03:56) Mihir’s finance automation team uses Airflow to tackle month-end bottlenecks. (06:00) Airflow's flexibility enables end-to-end automation for finance workflows. (07:00) Open-source Airflow tools offer cost-effective solutions for new teams. (08:46) Sensors and dynamic DAGs are pivotal features for optimizing tasks. (13:30) GitSync simplifies development by syncing environments seamlessly. (16:27) Plans include integrating Databricks for more advanced data handling. (17:58) Airflow and Databricks offer multiple flexible methods to trigger workflows and execute SQL queries seamlessly. Resources Mentioned: Mihir Samant - https://www.linkedin.com/in/misamant/?originalSubdomain=ca Etraveli Group - https://www.linkedin.com/company/etraveli-group/ Apache Airflow - https://airflow.apache.org/ Docker - https://www.docker.com/ Databricks - https://www.databricks.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…

1
Inside Ford’s Data Transformation: Advanced Orchestration Strategies with Vasantha Kosuri-Marshall 38:54

לפני 15 weeks38:54

38:54

Data engineering is entering a new era, where orchestration and automation are redefining how large-scale projects operate. This episode features Vasantha Kosuri-Marshall , Data and ML Ops Engineer at Ford Motor Company . Vasantha shares her expertise in managing complex data pipelines. She takes us through Ford's transition to cloud platforms, the adoption of Airflow and the intricate challenges of orchestrating data in a diverse environment. Key Takeaways: (03:10) Vasantha’s transition to the Advanced Driving Assist Systems team at Ford. (05:42) Early adoption of Airflow to orchestrate complex data pipelines. (09:29) Ford's move from on-premise data solutions to Google Cloud Platform. (12:03) The importance of Airflow's scheduling capabilities for efficient data management. (16:12) Using Kubernetes to scale Airflow for large-scale data processing. (19:59) Vasantha’s experience in overcoming challenges with legacy orchestration tools. (22:22) Integration of data engineering and data science pipelines at Ford. (28:03) How deferrable operators in Airflow improve performance and save costs. (32:12) Vasantha’s insights into tuning Airflow properties for thousands of DAGs. (36:09) The significance of monitoring and observability in managing Airflow instances. Resources Mentioned: Vasantha Kosuri-Marshall - https://www.linkedin.com/in/vasantha-kosuri-marshall-0b0aab188/ Apache Airflow - https://airflow.apache.org/ Google Cloud Platform (GCP) - https://cloud.google.com/ Ford Motor Company | LinkedIn - https://www.linkedin.com/company/ford-motor-company/ Ford Motor Company | Website - https://www.ford.com/ Astronomer - https://www.astronomer.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…

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