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

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محتوای ارائه شده توسط The Data Flowcast. تمام محتوای پادکست شامل قسمت‌ها، گرافیک‌ها و توضیحات پادکست مستقیماً توسط The Data Flowcast یا شریک پلتفرم پادکست آن‌ها آپلود و ارائه می‌شوند. اگر فکر می‌کنید شخصی بدون اجازه شما از اثر دارای حق نسخه‌برداری شما استفاده می‌کند، می‌توانید روندی که در اینجا شرح داده شده است را دنبال کنید.https://fa.player.fm/legal

The Data Flowcast: Mastering Apache Airflow ® for Data Engineering and AI «
Season One Teaser

۷ سال پیش 3:02

اشتراک گذاری

MP3•خانه قسمت

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

59 قسمت

#Tech #Airflow

Season One Teaser

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

published ۷ سال پیش

اشتراک گذاری

MP3•خانه قسمت

59 قسمت

#Tech #Airflow

همه قسمت ها

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

۴ روز پیش24:44

24:44

In this episode, we’re joined by IBM ’s Senior Product Manager, BJ Adesoji , and Chief Marketing Officer, 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…

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

11 روز پیش31:02

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…

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

18 روز پیش22:13

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…

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

25 روز پیش38:54

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…

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

۵ weeks پیش30:28

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…

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

۶ weeks پیش22:49

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…

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

۷ weeks پیش27:28

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…

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

۸ weeks پیش25:14

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

۹ weeks پیش27:42

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

10 weeks پیش23:26

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

11 weeks پیش26:15

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

12 weeks پیش28:27

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

13 weeks پیش36:46

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

14 weeks پیش27:40

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

14 weeks پیش17:54

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
The Intersection of AI and Data Management at Dosu with Devin Stein 20:18

35 weeks پیش20:18

20:18

Unlocking engineering productivity goes beyond coding — it’s about managing knowledge efficiently. In this episode, we explore the innovative ways in which Dosu leverages Airflow for data orchestration and supports the Airflow project. Devin Stein , Founder of Dosu , shares his insights on how engineering teams can focus on value-added work by automating knowledge management. Devin dives into Dosu’s purpose, the significance of AI in their product, and why they chose Airflow as the backbone for scheduling and data management. Key Takeaways: (01:33) Dosu's mission to democratize engineering knowledge. (05:00) AI is central to Dosu's product for structuring engineering knowledge. (06:23) The importance of maintaining up-to-date data for AI effectiveness. (07:55) How Airflow supports Dosu’s data ingestion and automation processes. (08:45) The reasoning behind choosing Airflow over other orchestrators. (11:00) Airflow enables Dosu to manage both traditional ETL and dynamic workflows. (13:04) Dosu assists the Airflow project by auto-labeling issues and discussions. (14:56) Thoughtful collaboration with the Airflow community to introduce AI tools. (16:37) The potential of Airflow to handle more dynamic, scheduled workflows in the future. (18:00) Challenges and custom solutions for implementing dynamic workflows in Airflow. Resources Mentioned: Apache Airflow - https://airflow.apache.org/ Dosu Website - https://dosu.dev/ 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
AI-Powered Vehicle Automation at Ford Motor Company with Serjesh Sharma 26:11

38 weeks پیش26:11

26:11

Harnessing data at scale is the key to driving innovation in autonomous vehicle technology. In this episode, we uncover how advanced orchestration tools are transforming machine learning operations in the automotive industry. Serjesh Sharma, Supervisor ADAS Machine Learning Operations (MLOps) at Ford Motor Company, joins us to discuss the challenges and innovations his team faces working to enhance vehicle safety and automation. Serjesh shares insights into the intricate data processes that support Ford’s Advanced Driver Assistance Systems (ADAS) and how his team leverages Apache Airflow to manage massive data loads efficiently. Key Takeaways: (01:44) ADAS involves advanced features like pre-collision assist and self-driving capabilities. (04:47) Ensuring sensor accuracy and vehicle safety requires extensive data processing. (05:08) The combination of on-prem and cloud infrastructure optimizes data handling. (09:27) Ford processes around one petabyte of data per week, using both CPUs and GPUs. (10:33) Implementing software engineering best practices to improve scalability and reliability. (15:18) GitHub Issues streamline onboarding and infrastructure provisioning. (17:00) Airflow's modular design allows Ford to manage complex data pipelines. (19:00) Kubernetes pod operators help optimize resource usage for CPU-intensive tasks. (20:35) Ford's scale challenges led to customized Airflow configurations for high concurrency. (21:02) Advanced orchestration tools are pivotal in managing vast data landscapes in automotive innovation. Resources Mentioned: Serjesh Sharma - www.linkedin.com/in/serjeshsharma/ Ford Motor Company - www.linkedin.com/company/ford-motor-company/ Apache Airflow - airflow.apache.org/ Kubernetes - 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
From Task Failures to Operational Excellence at GumGum with Brendan Frick 24:06

39 weeks پیش24:06

24:06

Data failures are inevitable but how you manage them can define the success of your operations. In this episode, we dive deep into the challenges of data engineering and AI with Brendan Frick, Senior Engineering Manager, Data at GumGum. Brendan shares his unique approach to managing task failures and DAG issues in a high-stakes ad-tech environment. Brendan discusses how GumGum leverages Apache Airflow to streamline data processes, ensuring efficient data movement and orchestration while minimizing disruptions in their operations. Key Takeaways: (02:02) Brendan’s role at GumGum and its approach to ad tech. (04:27) How GumGum uses Airflow for daily data orchestration, moving data from S3 to warehouses. (07:02) Handling task failures in Airflow using Jira for actionable, developer-friendly responses. (09:13) Transitioning from email alerts to a more structured system with Jira and PagerDuty. (11:40) Monitoring task retry rates as a key metric to identify potential issues early. (14:15) Utilizing Looker dashboards to track and analyze task performance and retry rates. (16:39) Transitioning from Kubernetes operator to a more reliable system for data processing. (19:25) The importance of automating stakeholder communication with data lineage tools like Atlan. (20:48) Implementing data contracts to ensure SLAs are met across all data processes. (22:01) The role of scalable SLAs in Airflow to ensure data reliability and meet business needs. Resources Mentioned: Brendan Frick - https://www.linkedin.com/in/brendan-frick-399345107/ GumGum - https://www.linkedin.com/company/gumgum/ Apache Airflow - https://airflow.apache.org/ Jira - https://www.atlassian.com/software/jira Atlan - https://atlan.com/ 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
From Sensors to Datasets: Enhancing Airflow at Astronomer with Maggie Stark and Marion Azoulai 22:25

40 weeks پیش22:25

22:25

A 13% reduction in failure rates — this is how two data scientists at Astronomer revolutionized their data pipelines using Apache Airflow. In this episode, we enter the world of data orchestration and AI with Maggie Stark and Marion Azoulai, both Senior Data Scientists at Astronomer. Maggie and Marion discuss how their team re-architected their use of Airflow to improve scalability, reliability and efficiency in data processing. They share insights on overcoming challenges with sensors and how moving to datasets transformed their workflows. Key Takeaways: (02:23) The data team’s role as a centralized hub within Astronomer. (05:11) Airflow is the backbone of all data processes, running 60,000 tasks daily. (07:13) Custom task groups enable efficient code reuse and adherence to best practices. (11:33) Sensor-heavy architectures can lead to cascading failures and resource issues. (12:09) Switching to datasets has improved reliability and scalability. (14:19) Building a control DAG provides end-to-end visibility of pipelines. (16:42) Breaking down DAGs into smaller units minimizes failures and improves management. (19:02) Failure rates improved from 16% to 3% with the new architecture. Resources Mentioned: Maggie Stark - https://www.linkedin.com/in/margaretstark/ Marion Azoulai - https://www.linkedin.com/in/marionazoulai/ Astronomer | LinkedIn - https://www.linkedin.com/company/astronomer/ Apache Airflow - https://airflow.apache.org/ Astronomer | Website - 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…

1
Mastering Data Orchestration with Airflow at M Science with Ben Tallman 24:36

41 weeks پیش24:36

24:36

Mastering the flow of data is essential for driving innovation and efficiency in today’s competitive landscape. In this episode, we explore the evolution of data orchestration and the pivotal role of Apache Airflow in modern data workflows. Ben Tallman, Chief Technology Officer at M Science, joins us and shares his extensive experience with Airflow, detailing its early adoption, evolution and the profound impact it has had on data engineering practices. His insights reveal how leveraging Airflow can streamline complex data processes, enhance observability and ultimately drive business success. Key Takeaways: (02:31) Benjamin’s journey with Airflow and its early adoption. (05:36) The transition from legacy schedulers to Airflow at Apigee and later Google. (08:52) The challenges and benefits of running production-grade Airflow instances. (10:46) How Airflow facilitates the management of large-scale data at M Science. (11:56) The importance of reducing time to value for customers using data products. (13:32) Airflow’s role in ensuring observability and reliability in data workflows. (17:00) Managing petabytes of data and billions of records efficiently. (19:08) Integration of various data sources and ensuring data product quality. (20:04) Leveraging Airflow for data observability and reducing time to value. (22:04) Benjamin’s vision for the future development of Airflow, including audit trails for variables. Resources Mentioned: Ben Tallman - https://www.linkedin.com/in/btallman/ M Science - https://www.linkedin.com/company/m-science-llc/ Apache Airflow - https://airflow.apache.org/ Astronomer - https://www.astronomer.io/ Databricks - https://databricks.com/ Snowflake - https://www.snowflake.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
Welcome to The Data Flowcast 2:01

42 weeks پیش2:01

2:01

Welcome to The Data Flowcast: Mastering Airflow for Data Engineering & 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. #AI #Automation #Airflow #MachineLearning…

1
Enhancing Business Metrics With Airflow at Artlist with Hannan Kravitz 23:51

42 weeks پیش23:51

23:51

Data orchestration is revolutionizing the way companies manage and process data. In this episode, we explore the critical role of data orchestration in modern data workflows and how Apache Airflow is used to enhance data processing and AI model deployment. Hannan Kravitz, Data Engineering Team Leader at Artlist, joins us to share his insights on leveraging Airflow for data engineering and its impact on their business operations. Key Takeaways: (01:00) Hannan introduces Artlist and its mission to empower content creators. (04:27) The importance of collecting and modeling data to support business insights. (06:40) Using Airflow to connect multiple data sources and create dashboards. (09:40) Implementing a monitoring DAG for proactive alerts within Airflow. (12:31) Customizing Airflow for business metric KPI monitoring and setting thresholds. (15:00) Addressing decreases in purchases due to technical issues with proactive alerts. (17:45) Customizing data quality checks with dynamic task mapping in Airflow. (20:00) Desired improvements in Airflow UI and logging capabilities. (21:00) Enabling business stakeholders to change thresholds using Streamlit. (22:26) Future improvements desired in the Airflow project. Resources Mentioned: Hannan Kravitz - https://www.linkedin.com/in/hannan-kravitz-60563112/ Artlist - https://www.linkedin.com/company/art-list/ Apache Airflow - https://airflow.apache.org/ Snowflake - https://www.snowflake.com/ Streamlit - https://streamlit.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
Cutting-Edge Data Engineering at Teya with Alexandre Magno Lima Martins 23:46

43 weeks پیش23:46

23:46

Data engineering is constantly evolving and staying ahead means mastering tools like Apache Airflow. In this episode, we explore the world of data engineering with Alexandre Magno Lima Martins, Senior Data Engineer at Teya. Alexandre talks about optimizing data workflows and the smart solutions they've created at Teya to make data processing easier and more efficient. Key Takeaways: (02:01) Alexandre explains his role at Teya and the responsibilities of a data platform engineer. (02:40) The primary use cases of Airflow at Teya, especially with dbt and machine learning projects. (04:14) How Teya creates self-service DAGs for dbt models. (05:58) Automating DAG creation with CI/CD pipelines. (09:04) Switching to a multi-file method for better Airflow performance. (12:48) Challenges faced with Kubernetes Executor vs. Celery Executor. (16:13) Using Celery Executor to handle fast tasks efficiently. (17:02) Implementing KEDA autoscaler for better scaling of Celery workers. (19:05) Reasons for not using Cosmos for DAG generation and cross-DAG dependencies. (21:16) Alexandre's wish list for future Airflow features, focusing on multi-tenancy. Resources Mentioned: Alexandre Magno Lima Martins - https://www.linkedin.com/in/alex-magno/ Teya - https://www.linkedin.com/company/teya-global/ Apache Airflow - https://airflow.apache.org/ dbt - https://www.getdbt.com/ Kubernetes - https://kubernetes.io/ KEDA - https://keda.sh/ 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 Strategies for Business Efficiency at Campbell with Larry Komenda 26:10

45 weeks پیش26:10

26:10

Managing data workflows well can change the game for any company. In this episode, we talk about how Airflow makes this possible. Larry Komenda, Chief Technology Officer at Campbell, shares how Airflow supports their operations and improves efficiency. Larry discusses his role at Campbell, their switch to Airflow, and its impact. We look at their strategies for testing and maintaining reliable workflows and how these help their business. Key Takeaways: (02:26) Strong technology and data systems are crucial for Campbell’s investment process. (05:03) Airflow manages data pipelines efficiently in the market data team. (07:39) Airflow supports various departments, including trading and operations. (09:22) Machine learning models run on dedicated Airflow instances. (11:12) Reliable workflows are ensured through thorough testing and development. (13:45) Business tasks are organized separately from Airflow for easier testing. (15:30) Non-technical teams have access to Airflow for better efficiency. (17:20) Thorough testing before deploying to Airflow is essential. (19:10) Non-technical users can interact with Airflow DAGs to solve their issues. (21:55) Airflow improves efficiency and reliability in trading and operations. (24:40) Enhancing the Airflow UI for non-technical users is important for accessibility. Resources Mentioned: Larry Komenda - https://www.linkedin.com/in/larrykomenda/ Campbell - https://www.linkedin.com/company/campbell-and-company/ 30% off Airflow Summit Ticket - https://ti.to/airflowsummit/2024/discount/30DISC_ASTRONOMER Apache Airflow - https://airflow.apache.org/ NumPy - https://numpy.org/ Python - https://www.python.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
How Laurel Uses Airflow To Enhance Machine Learning Pipelines with Vincent La and Jim Howard 23:58

47 weeks پیش23:58

23:58

The world of timekeeping for knowledge workers is transforming through the use of AI and machine learning. Understanding how to leverage these technologies is crucial for improving efficiency and productivity. In this episode, we’re joined by Vincent La, Principal Data Scientist at Laurel, and Jim Howard, Principal Machine Learning Engineer at Laurel, to explore the implementation of AI in automating timekeeping and its impact on legal and accounting firms. Key Takeaways: (01:54) Laurel's mission in time automation. (03:39) Solving clustering, prediction and summarization with AI. (06:30) Daily batch jobs for user time generation. (08:22) Knowledge workers touch 300 items daily. (09:01) Mapping 300 activities to seven billable items. (11:38) Retraining models for better performance. (14:00) Using Airflow for retraining and backfills. (17:06) RAG-based summarization for user-specific tone. (18:58) Testing Airflow DAGs for cost-effective summarization. (22:00) Enhancing Airflow for long-running DAGs. Resources Mentioned: Vincent La - https://www.linkedin.com/in/vincentla/ Jim Howard - https://www.linkedin.com/in/jameswhowardml/ Laurel - https://www.linkedin.com/company/laurel-ai/ Apache Airflow - https://airflow.apache.org/ Ernst & Young - https://www.ey.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
How Vibrant Planet's Self-Healing Pipelines Revolutionize Data Processing 23:51

49 weeks پیش23:51

23:51

Discover the cutting-edge methods Vibrant Planet uses to revolutionize geospatial data processing and resource management. In this episode, we delve into the intricacies of scaling geospatial data processing and resource allocation with experts from Vibrant Planet. Joining us are Cyrus Dukart, Engineering Lead, and David Sacerdote, Staff Software Engineer, who share their innovative approaches to handling large datasets and optimizing resource use in Airflow. Key Takeaways: (00:00) Inefficiencies in resource allocation. (03:00) Scientific validity of sharded results. (05:53) Tech-based solutions for resource management. (06:11) Retry callback process for resource allocation. (08:00) Running database queries for resource needs. (10:05) Importance of remembering resource usage. (13:51) Generating resource predictions. (14:44) Custom task decorator for resource management. (20:28) Massive resource usage gap in sharded data. (21:14) Fail-fast model for long-running tasks. Resources Mentioned: Cyrus Dukart - https://www.linkedin.com/in/cyrus-dukart-6561482/ David Sacerdote - https://www.linkedin.com/in/davidsacerdote/ Vibrant Planet - https://www.linkedin.com/company/vibrant-planet/ Apache Airflow - https://airflow.apache.org/ Kubernetes - https://kubernetes.io/ Vibrant Planet - https://vibrantplanet.net/ 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 Future of AI in Data Engineering With Astronomer’s Julian LaNeve and David Xue 23:36

1 year پیش23:36

23:36

The world of data orchestration and machine learning is rapidly evolving, and tools like Apache Airflow are at the forefront of these changes. Understanding how to effectively utilize these tools can significantly enhance data processing and AI model deployment. This episode features Julian LaNeve, CTO at Astronomer, and David Xue, Machine Learning Engineer at Astronomer. They delve into the intricacies of data orchestration, generative AI and the practical applications of these technologies in modern data workflows. Key Takeaways: (01:51) The pressure to engage in the generative AI space. (02:02) Generative AI can elevate data utilization to the next level. (02:43) The transparency issues with commercial AI models. (04:27) High-quality data in model performance is crucial. (06:40) Running new models on smaller devices, like phones. (12:19) Fine-tuning LLMs to handle millions of task failures. (16:54) Teaching AI to understand specific logs, not general passages, is a goal. (21:56) Using Airflow as a general-purpose orchestration tool. (22:00) Airflow is adaptable for various use cases, including ETL and ML systems. Resources Mentioned: Julian LaNeve - https://www.linkedin.com/in/julianlaneve/ Atronomer - https://www.linkedin.com/company/astronomer/ David Xue - https://www.linkedin.com/in/david-xue-uva/ Apache Airflow - https://airflow.apache.org/ Meta’s Open Source Llama 3 model: https://ai.meta.com/blog/meta-llama-3/https://ai.meta.com/blog/meta-llama-3/ Microsoft’s Phi-3 model: https://www.microsoft.com/en-us/research/publication/phi-3-technical-report-a-highly-capable-language-model-locally-on-your-phone/ GPT-4 - https://www.openai.com/research/gpt-4 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 Power of Airflow in Modern Data Environments at Wynn Las Vegas with Siva Krishna Yetukuri 24:31

1 year پیش24:31

24:31

Understanding the critical role of data integration and management is essential for driving business success, particularly in a dynamic environment like a luxury casino resort. In this episode, we sit down with Siva Krishna Yetukuri, Cloud Data Architect at Wynn Las Vegas, to explore how Airflow and other tools are transforming data workflows and customer experiences at Wynn Las Vegas. Key Takeaways: (02:00) Siva designs and builds cutting-edge data pipelines and architectures. (02:54) Wynn is building a data platform to drive surveys and marketing strategies. (05:00) Airflow is the backbone of data ingestion, curation and integration. (07:00) Custom operators in Airflow enhance monitoring and reporting. (09:00) Excitement surrounds the use of Airflow 2.9 and its new features. (08:32) A metadata database drives Airflow workflows and captures metrics. (12:31) Understanding Airflow fundamentals in layman’s terms simplifies complexity. (16:33) Transitioning from Control-M to Airflow eases building complex workflows. (24:06) ML models for volume and freshness anomalies improve data quality. (20:15) DAGs are often auto-generated, simplifying the process for engineers. Resources Mentioned: Apache Airflow - https://airflow.apache.org/ Snowflake - https://www.snowflake.com/ Databricks - https://databricks.com/ Great Expectations - https://greatexpectations.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
Powering the Texas Rangers World Series Win With AI on Airflow with Alexander Booth 23:38

1 year پیش23:38

23:38

The integration of data and AI in sports is transforming how teams strategize and perform. Understanding how to harness this technology is key to staying competitive in the rapidly evolving landscape of baseball. In this episode, we sit down with Alexander Booth, Assistant Director of Research and Development at Texas Rangers Baseball Club, to explore the intersection of big data, AI and baseball strategy. Key Takeaways: (03:00) Alexander Booth's role and responsibilities at the Texas Rangers. (03:33) The implementation of multiple cameras and pose tracking in stadiums. (06:16) The importance of Airflow in organizing data orchestrations. (06:22) The demand for faster data among modern baseball players. (11:01) The necessity of scalable solutions for handling large data sets. (15:00) How weather data influences game strategy. (15:46) The impact of advanced technology on decision-making in baseball. (18:00) The role of AI and machine learning in player and game analysis. (22:26) The use of dynamic tasks in Airflow for better data management. Resources Mentioned: Apache Airflow - https://airflow.apache.org/ Statcast - https://www.mlb.com/statcast Google BigQuery - https://cloud.google.com/bigquery/ Databricks - https://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
Expanding the Data Engineering Toolkit at Reddit 45:48

۴ years پیش45:48

45:48

Welcome back to the Airflow Podcast. This week, we met up with Ben Wisegarver, a staff data scientist at Reddit who runs their data warehousing and data engineering functions. Reddit users generate petabytes of data every day that needs to be processed, stored, and analyzed by a wide breadth of backend services. Our conversation with Ben touches on everything from Airflow as a tool for career mobility across the data stack to scaling out a self-service data architecture across many teams. For folks interested, our team at Astronomer is growing rapidly and we're on the hunt for new folks to join in a variety of different roles. If you're passionate about Airflow and interested in building the future of data engineering, please get in touch. You can check our current job postings at careers.astronomer.io, but we're constantly updating our listings to accommodate new hiring needs. Please feel free to email me directly at pete@astronomer.io if you're passionate about what we're doing and think you'd be a good addition to the team. Mentioned Resources: Careers: https://careers.astronomer.io Guest Profile: Ben Wisegarver: https://www.linkedin.com/in/ben-wisegarver-54566576…

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

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

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The Data Flowcast: Mastering Apache Airflow ® for Data Engineering and AI «
Season One Teaser