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We Have The Receipts
1 Battle Camp S1: Reality Rivalries with Dana Moon & QT 1:00:36
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Leveraging Airflow To Build Scalable and Reliable Data Platforms at 99acres.com with Samyak Jain
Manage episode 467650550 series 2053958
Content provided by The Data Flowcast. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by The Data Flowcast or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.
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:
https://www.linkedin.com/in/samyak-jain-ab5830169/
https://www.linkedin.com/company/99acres/
https://airflow.apache.org/
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
56 episodes
Leveraging Airflow To Build Scalable and Reliable Data Platforms at 99acres.com with Samyak Jain
The Data Flowcast: Mastering Apache Airflow ® for Data Engineering and AI
ShareManage episode 467650550 series 2053958
Content provided by The Data Flowcast. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by The Data Flowcast or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.
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:
https://www.linkedin.com/in/samyak-jain-ab5830169/
https://www.linkedin.com/company/99acres/
https://airflow.apache.org/
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
56 episodes
All episodes
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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
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Play Later Play Later Lists Like LikedBuilding 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…
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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
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Play Later Play Later Lists Like LikedCreating 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…
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The Data Flowcast: Mastering Apache Airflow ® for Data Engineering and AI
1 Data Quality and Observability at Tekmetric with Ipsa Trivedi 22:49
22:49 Play Later Play Later Lists Like Liked22:49
Play Later Play Later Lists Like LikedAirflow’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…
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The Data Flowcast: Mastering Apache Airflow ® for Data Engineering and AI
1 Introducing Apache Airflow® 3 with Vikram Koka and Jed Cunningham 27:28
27:28 Play Later Play Later Lists Like Liked27:28
Play Later Play Later Lists Like LikedThe 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…
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The Data Flowcast: Mastering Apache Airflow ® for Data Engineering and AI
1 Airflow in Action: Powering Instacart's Complex Ecosystem 25:14
25:14 Play Later Play Later Lists Like Liked25:14
Play Later Play Later Lists Like LikedThe 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…
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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
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Play Later Play Later Lists Like LikedData 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…
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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
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Play Later Play Later Lists Like LikedThe 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…
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The Data Flowcast: Mastering Apache Airflow ® for Data Engineering and AI
1 Airflow’s Role in the Rise of DataOps with Andy Byron 26:15
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Play Later Play Later Lists Like LikedThe 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…
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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
28:27 Play Later Play Later Lists Like Liked28:27
Play Later Play Later Lists Like LikedThe 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…
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The Data Flowcast: Mastering Apache Airflow ® for Data Engineering and AI
1 Building Scalable ML Infrastructure at Outerbounds with Savin Goyal 36:46
36:46 Play Later Play Later Lists Like Liked36:46
Play Later Play Later Lists Like LikedMachine 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…
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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
27:40 Play Later Play Later Lists Like Liked27:40
Play Later Play Later Lists Like LikedKeeping 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…
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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
17:54 Play Later Play Later Lists Like Liked17:54
Play Later Play Later Lists Like LikedTurning 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…
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The Data Flowcast: Mastering Apache Airflow ® for Data Engineering and AI
1 Leveraging Airflow To Build Scalable and Reliable Data Platforms at 99acres.com with Samyak Jain 25:08
25:08 Play Later Play Later Lists Like Liked25:08
Play Later Play Later Lists Like LikedData 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…
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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
33:45 Play Later Play Later Lists Like Liked33:45
Play Later Play Later Lists Like LikedTesting 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…
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The Data Flowcast: Mastering Apache Airflow ® for Data Engineering and AI
1 Overcoming Airflow Scaling Challenges at Monzo Bank with Jonathan Rainer 43:39
43:39 Play Later Play Later Lists Like Liked43:39
Play Later Play Later Lists Like LikedScaling 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…
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