Building Resilient Data Systems for Modern Enterprises at Astrafy with Andrea Bombino

The Data Flowcast: Mastering Airflow for Data Engineering & AI

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Nội dung được cung cấp bởi The Data Flowcast. Tất cả nội dung podcast bao gồm các tập, đồ họa và mô tả podcast đều được The Data Flowcast hoặc đối tác nền tảng podcast của họ tải lên và cung cấp trực tiếp. Nếu bạn cho rằng ai đó đang sử dụng tác phẩm có bản quyền của bạn mà không có sự cho phép của bạn, bạn có thể làm theo quy trình được nêu ở đây https://vi.player.fm/legal.

Rethinking Alzheimer's Disease Podcast

1
Ep 1: What Is Alzheimer’s Disease? 16:03

cách đây 51 weeks trước16:03

Nghe Sau

Danh sách

Thích

Đã thích

16:03

Learn about the differences between Alzheimer's and dementia, and how Alzheimer's disease progresses. We talk about why catching the disease early can make a big difference. Dr. Sharon Cohen and Dr. Yaakov Stern walk us through the stages of Alzheimer's disease, from when there are no symptoms to when memory issues start to show. They explain the stages of Alzheimer’s and how it develops over time. We also hear from Kelly, who explains her personal experiences and concerns about developing Alzheimer’s, and what she does about it. For links to resources and information covered in this series, visit our website at HealthUnmuted.com/resources What did you think of this episode? We’d love to hear from you. Please visit healthunmuted.com/feedback to let us know! Rethinking Alzheimer’s Disease was made possible with support from Eisai Inc. [00:00:00] Introduction [00:03:10] What's the difference between Alzheimer's disease and dementia? [00:07:04] When does Alzheimer’s begin to develop? [00:09:08] What is Mild Cognitive Impairment (MCI)? [00:10:36] What is subjective cognitive decline? [00:11:59] What is preclinical Alzheimer's disease? [00:13:13] Why is it important to detect Alzheimer’s disease early? Disclaimer: The content provided in this podcast is intended for informational purposes only. It is not a substitute for professional medical advice, diagnosis, or treatment. Always seek the advice of your physician or other qualified healthcare provider with any questions you may have regarding a medical condition. Never disregard professional medical advice or delay in seeking it because of something you have heard on this podcast. Reliance on any information provided by this podcast or its guests is solely at your own risk. ©2024 Mission Based Media Ltd • April 2024 • AD-M2059…

cách đây khoảng một năm trước 28:29

MP3•Trang chủ episode

Key Takeaways:

(01:55) Astrafy helps companies manage data using Google Cloud.

(04:36) Airflow is central to Astrafy’s data engineering efforts.

(07:17) Datasets and Pub/Sub are used for real-time workflows.

(09:59) Pub/Sub links multiple Airflow environments.

(12:40) Datasets eliminate the need for constant monitoring.

(15:22) Airflow updates have improved large-scale data operations.

(18:03) New Airflow API features make dataset updates easier.

(20:45) Real-time orchestration speeds up data processing for clients.

(23:26) Pub/Sub enhances flexibility across cloud environments.

(26:08) Future Airflow features will offer more control over data workflows.

Resources Mentioned:

Andrea Bombino -

https://www.linkedin.com/in/andrea-bombino/

Astrafy -

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

Apache Airflow -

https://airflow.apache.org/

Google Cloud -

https://cloud.google.com/

dbt -

https://www.getdbt.com/

Apache Airflow Survey -

https://astronomer.typeform.com/airflowsurvey24

Thanks for listening to “The Data Flowcast: Mastering Airflow for Data Engineering & AI.” If you enjoyed this episode, please leave a 5-star review to help get the word out about the show. And be sure to subscribe so you never miss any of the insightful conversations.

#AI #Automation #Airflow #MachineLearning

49 tập

#IT Management #Tech #Industries #Airflow

Building Resilient Data Systems for Modern Enterprises at Astrafy with Andrea Bombino

The Data Flowcast: Mastering Airflow for Data Engineering & AI

39 subscribers

published cách đây khoảng một năm trước

Chia sẻ

MP3•Trang chủ episode

Key Takeaways:

(01:55) Astrafy helps companies manage data using Google Cloud.

(04:36) Airflow is central to Astrafy’s data engineering efforts.

(07:17) Datasets and Pub/Sub are used for real-time workflows.

(09:59) Pub/Sub links multiple Airflow environments.

(12:40) Datasets eliminate the need for constant monitoring.

(15:22) Airflow updates have improved large-scale data operations.

(18:03) New Airflow API features make dataset updates easier.

(20:45) Real-time orchestration speeds up data processing for clients.

(23:26) Pub/Sub enhances flexibility across cloud environments.

(26:08) Future Airflow features will offer more control over data workflows.

Resources Mentioned:

Andrea Bombino -

https://www.linkedin.com/in/andrea-bombino/

Astrafy -

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

Apache Airflow -

https://airflow.apache.org/

Google Cloud -

https://cloud.google.com/

dbt -

https://www.getdbt.com/

Apache Airflow Survey -

https://astronomer.typeform.com/airflowsurvey24

#AI #Automation #Airflow #MachineLearning

49 tập

#IT Management #Tech #Industries #Airflow

Tất cả các tập

The Data Flowcast: Mastering Airflow for Data Engineering & AI

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

cách đây 5 ngày trước26:15

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…

The Data Flowcast: Mastering Airflow for Data Engineering & AI

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

cách đây 12 ngày trước28:27

28:27

The security of open-source software is a growing concern, especially as dependencies and regulations become more complex, making it essential to understand how to manage software supply chains effectively. In this episode, we sit down with Michael Winser , Co-Founder at Alpha-Omega and Security Strategy Ambassador at Eclipse Foundation , and Jarek Potiuk , Member of the Security Committee at the Apache Software Foundation , to discuss the challenges of securing Airflow’s dependencies, the evolving landscape of open-source security and how contributors can help strengthen the ecosystem. Key Takeaways: (02:43) Jarek quit his full-time engineer position and uses Airflow as a freelancer. (04:32) Michael finds happiness in having meaningful work with open-source security. (07:01) Software supply chain security focuses on correctness, integrity and availability. (08:44) Airflow’s 790 dependencies present a unique security challenge. (09:43) Airflow’s security team has significantly improved its vulnerability response. (10:22) The transition to Airflow 3 emphasizes enterprise security readiness. (16:20) The ‘Three Fs’ approach: fix it, fork it, or forget it. (18:45) Dependency health is often more critical than fixing known vulnerabilities. (23:32) The ‘Three Fs’ in action. (26:26) Open-source contributors play a key role in supply chain security. Resources Mentioned: Michael Winser - https://www.linkedin.com/in/michaelw/ Jarek Potiuk - https://www.linkedin.com/in/jarekpotiuk/ Apache Airflow - https://airflow.apache.org/ Apache Software Foundation | LinkedIn - https://www.linkedin.com/company/the-apache-software-foundation/ Apache Software Foundation | Website - https://www.apache.org/ Eclipse Foundation | LinkedIn - https://www.linkedin.com/company/eclipse-foundation/ Eclipse Foundation | Website - https://www.eclipse.org/org/foundation/ OpenSSF Working Groups - https://openssf.org/community/openssf-working-groups/ Astronomer Roadshow: Exploring Apache Airflow 3 | London https://www.astronomer.io/events/roadshow/london/ Astronomer Roadshow: Exploring Apache Airflow 3 | New York https://www.astronomer.io/events/roadshow/new-york/ Astronomer Roadshow: Exploring Apache Airflow 3 | Sydney https://www.astronomer.io/events/roadshow/sydney/ Astronomer Roadshow: Exploring Apache Airflow 3 | San Francisco https://www.astronomer.io/events/roadshow/san-francisco/ Astronomer Roadshow: Exploring Apache Airflow 3 | Chicago https://www.astronomer.io/events/roadshow/chicago/ Thanks for listening to “The Data Flowcast: Mastering Airflow for Data Engineering & AI.” If you enjoyed this episode, please leave a 5-star review to help get the word out about the show. And be sure to subscribe so you never miss any of the insightful conversations. #AI #Automation #Airflow #MachineLearning…

The Data Flowcast: Mastering Airflow for Data Engineering & AI

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

cách đây 19 ngày trước36:46

36:46

Machine learning is changing fast, and companies need better tools to handle AI workloads. The right infrastructure helps data scientists focus on solving problems instead of managing complex systems. In this episode, we talk with Savin Goyal , Co-Founder and CTO at Outerbounds , about building ML infrastructure, how orchestration makes workflows easier and how Metaflow and Airflow work together to simplify data science. Key Takeaways: (02:02) Savin spent years building AI and ML infrastructure, including at Netflix. (04:05) ML engineering was not a defined role a decade ago. (08:17) Modernizing AI and ML requires balancing new tools with existing strengths. (10:28) ML workloads can be long-running or require heavy computation. (15:29) Different teams at Netflix used multiple orchestration systems for specific needs. (20:10) Stable APIs prevent rework and keep projects moving. (21:07) Metaflow simplifies ML workflows by optimizing data and compute interactions. (25:53) Limited local computing power makes running ML workloads challenging. (27:43) Airflow UI monitors pipelines, while Metaflow UI gives ML insights. (33:13) The most successful data professionals focus on business impact, not just technology. Resources Mentioned: Savin Goyal - https://www.linkedin.com/in/savingoyal/ Outerbounds - https://www.linkedin.com/company/outerbounds/ Apache Airflow - https://airflow.apache.org/ Metaflow - https://metaflow.org/ Netflix’s Maestro Orchestration System - https://netflixtechblog.com/maestro-netflixs-workflow-orchestrator-ee13a06f9c78?gi=8e6a067a92e9#:~:text=Maestro%20is%20a%20fully%20managed,data%20between%20different%20storages%2C%20etc. TensorFlow - https://www.tensorflow.org/ PyTorch - https://pytorch.org/ Thanks for listening to “The Data Flowcast: Mastering Airflow for Data Engineering & AI.” If you enjoyed this episode, please leave a 5-star review to help get the word out about the show. And be sure to subscribe so you never miss any of the insightful conversations. #AI #Automation #Airflow #MachineLearning…

The Data Flowcast: Mastering Airflow for Data Engineering & AI

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

cách đây 26 ngày trước27:40

27:40

Keeping data pipelines reliable at scale requires more than just the right tools — it demands constant innovation. In this episode, Nick Bilozerov , Senior Data Engineer at Stripe , and Sharadh Krishnamurthy , Engineering Manager at Stripe, discuss how Stripe customizes Airflow for its needs, the evolution of its data orchestration framework and the transition to Airflow 2. They also share insights on scaling data workflows while maintaining performance, reliability and developer experience. Key Takeaways: (02:04) Stripe’s mission is to grow the GDP of the internet by supporting businesses with payments and data. (05:08) 80% of Stripe engineers use data orchestration, making scalability critical. (06:06) Airflow powers business reports, regulatory needs and ML workflows. (08:02) Custom task frameworks improve dependencies and validation. (08:50) "User scope mode" enables local testing without production impact. (10:39) Migrating to Airflow 2 improves isolation, safety and scalability. (16:40) Monolithic DAGs caused database issues, prompting a service-based shift. (19:24) Frequent Airflow upgrades ensure stability and access to new features. (21:38) DAG versioning and backfill improvements enhance developer experience. (23:38) Greater UI customization would offer more flexibility. Resources Mentioned: Nick Bilozerov - https://www.linkedin.com/in/nick-bilozerov/ Sharadh Krishnamurthy - https://www.linkedin.com/in/sharadhk/ Apache Airflow - https://airflow.apache.org/ Stripe | LinkedIn - https://www.linkedin.com/company/stripe/ Stripe | Website - https://stripe.com/ Thanks for listening to “The Data Flowcast: Mastering Airflow for Data Engineering & AI.” If you enjoyed this episode, please leave a 5-star review to help get the word out about the show. And be sure to subscribe so you never miss any of the insightful conversations. #AI #Automation #Airflow #MachineLearning…

The Data Flowcast: Mastering Airflow for Data Engineering & AI

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

cách đây 5 weeks trước17:54

17:54

Turning complex datasets into meaningful analysis requires robust data infrastructure and seamless orchestration. In this episode, we’re joined by Jennifer Melot , Technical Lead at the Center for Security and Emerging Technology (CSET) at Georgetown University, to explore how Airflow powers data-driven insights in technology policy research. Jennifer shares how her team automates workflows to support analysts in navigating complex datasets. Key Takeaways: (02:04) CSET provides data-driven analysis to inform government decision-makers. (03:54) ETL pipelines merge multiple data sources for more comprehensive insights. (04:20) Airflow is central to automating and streamlining large-scale data ingestion. (05:11) Larger-scale databases create challenges that require scalable solutions. (07:20) Dynamic DAG generation simplifies Airflow adoption for non-engineers. (12:13) DAG Factory and dynamic task mapping can improve workflow efficiency. (15:46) Tracking data lineage helps teams understand dependencies across DAGs. (16:14) New Airflow features enhance visibility and debugging for complex pipelines. Resources Mentioned: Jennifer Melot - https://www.linkedin.com/in/jennifer-melot-aa710144/ Center for Security and Emerging Technology (CSET) - https://www.linkedin.com/company/georgetown-cset/ Apache Airflow - https://airflow.apache.org/ Zenodo - https://zenodo.org/ OpenLineage - https://openlineage.io/ Cloud Dataplex - https://cloud.google.com/dataplex Thanks for listening to “The Data Flowcast: Mastering Airflow for Data Engineering & AI.” If you enjoyed this episode, please leave a 5-star review to help get the word out about the show. And be sure to subscribe so you never miss any of the insightful conversations. #AI #Automation #Airflow #MachineLearning…

The Data Flowcast: Mastering Airflow for Data Engineering & AI

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

cách đây 6 weeks trước25:08

25:08

Data orchestration is evolving rapidly, with dynamic workflows becoming the cornerstone of modern data engineering. In this episode, we are joined by Samyak Jain , Senior Software Engineer - Big Data at 99acres.com . Samyak shares insights from his journey with Apache Airflow, exploring how his team built a self-service platform that enables non-technical teams to launch data pipelines and marketing campaigns seamlessly. Key Takeaways: (02:02) Starting a career in data engineering by troubleshooting Airflow pipelines. (04:27) Building self-service portals with Airflow as the backend engine. (05:34) Utilizing API endpoints to trigger dynamic DAGs with parameterized templates. (09:31) Managing a dynamic environment with over 1,400 active DAGs. (11:14) Implementing fault tolerance by segmenting data workflows into distinct layers. (14:15) Tracking and optimizing query costs in AWS Athena to save $7K monthly. (16:22) Automating cost monitoring with real-time alerts for high-cost queries. (17:15) Streamlining Airflow metadata cleanup to prevent performance bottlenecks. (21:30) Efficiently handling one-time and recurring marketing campaigns using Airflow. (24:18) Advocating for Airflow features that improve resource management and ownership tracking. Resources Mentioned: Samyak Jain - https://www.linkedin.com/in/samyak-jain-ab5830169/ 99acres.com - https://www.linkedin.com/company/99acres/ Apache Airflow - https://airflow.apache.org/ AWS Athena - https://aws.amazon.com/athena/ Kafka - https://kafka.apache.org/ Thanks for listening to “The Data Flowcast: Mastering Airflow for Data Engineering & AI.” If you enjoyed this episode, please leave a 5-star review to help get the word out about the show. And be sure to subscribe so you never miss any of the insightful conversations. #AI #Automation #Airflow #MachineLearning…

The Data Flowcast: Mastering Airflow for Data Engineering & AI

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

cách đây 7 weeks trước33:45

33:45

Testing autonomous vehicles demands precision, scalability and powerful orchestration tools — enter Apache Airflow, a key component of Bosch’s cutting-edge testing framework. In this episode, we sit down with Jens Scheffler , Test Execution Cluster Technical Architect, and Christian Schilling , Product Owner Open Loop Testing Automated Driving, both at Bosch , to explore how Bosch harnesses Airflow to streamline complex testing scenarios. They share insights on scaling workflows, integrating hybrid infrastructures and ensuring vehicle safety through rigorous automated testing. Key Takeaways: (01:35) Airflow orchestrates millions of test hours for autonomous systems. (03:15) Jens scales distributed systems with Kubernetes for job orchestration. (06:02) Airflow runs hundreds of tests simultaneously. (06:44) Virtual testing reduces costs and on-road trials. (12:19) Unified APIs and GUIs streamline operations. (15:05) Self-service setups empower Bosch teams. (18:00) Physical hardware integration ensures real-world timing. (20:30) Dynamic task mapping scales workflows efficiently. (25:22) Open-source contributions improve stability. (31:06) Edge and Celery executors power Bosch's hybrid scheduling. Resources Mentioned: Jens Scheffler - https://www.linkedin.com/in/jens-scheffler/ Christian Schilling - https://www.linkedin.com/in/christian-schilling-a5078831a/ Bosch - https://www.linkedin.com/company/bosch/ Apache Airflow - https://airflow.apache.org/ Kubernetes - https://kubernetes.io GitHub - https://github.com Edge Executor - https://airflow.apache.org/docs/apache-airflow/stable/core-concepts/executor/index.html Thanks for listening to “The Data Flowcast: Mastering Airflow for Data Engineering & AI.” If you enjoyed this episode, please leave a 5-star review to help get the word out about the show. And be sure to subscribe so you never miss any of the insightful conversations. #AI #Automation #Airflow #MachineLearning…

The Data Flowcast: Mastering Airflow for Data Engineering & AI

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

cách đây 8 weeks trước43:39

43:39

Scaling a data orchestration platform to manage thousands of tasks daily demands innovative solutions and strategic problem-solving. In this episode, we explore the complexities of scaling Airflow and the challenges of orchestrating thousands of tasks in dynamic data environments. Jonathan Rainer , Former Platform Engineer at Monzo Bank , joins us to share his journey optimizing data pipelines, overcoming UI limitations and ensuring DAG consistency in high-stakes scenarios. Key Takeaways: (03:11) Using Airflow to schedule computation in BigQuery. (07:02) How DAGs with 8,000+ tasks were managed nightly. (08:18) Ensuring accuracy in regulatory reporting for banking. (11:35) Handling task inconsistency and DAG failures with automation. (16:09) Building a service to resolve DAG consistency issues in Airflow. (25:05) Challenges with scaling the Airflow UI for thousands of tasks. (27:03) The role of upstream and downstream task management in Airflow. (37:33) The importance of operational metrics for monitoring Airflow health. (39:19) Balancing new tools with root cause analysis to address scaling issues. (41:35) Why scaling solutions require both technical and leadership buy-in Resources Mentioned: Jonathan Rainer - https://www.linkedin.com/in/jonathan-rainer/ Monzo Bank - https://www.linkedin.com/company/monzo-bank/ Apache Airflow - https://airflow.apache.org/ BigQuery - https://airflow.apache.org/docs/apache-airflow-providers-google/stable/operators/cloud/bigquery.html Kubernetes - https://kubernetes.io/ Thanks for listening to “The Data Flowcast: Mastering Airflow for Data Engineering & AI.” If you enjoyed this episode, please leave a 5-star review to help get the word out about the show. And be sure to subscribe so you never miss any of the insightful conversations. #AI #Automation #Airflow #MachineLearning…

The Data Flowcast: Mastering Airflow for Data Engineering & AI

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

cách đây 9 weeks trước26:00

26:00

T he future of data engineering lies in seamless orchestration and automation. In this episode, Arjun Anandkumar , Data Engineer at Telia , shares how his team uses Airflow to drive analytics and AI workflows. He highlights the challenges of scaling data platforms and how adopting best practices can simplify complex processes for teams across the organization. Arjun also discusses the transformative role of tools like Cosmos and Terraform in enhancing efficiency and collaboration. Key Takeaways: (02:16) Telia operates across the Nordics and Baltics, focusing on telecom and energy services. (03:45) Airflow runs dbt models seamlessly with Cosmos on AWS MWAA. (05:47) Cosmos improves visibility and orchestration in Airflow. (07:00) Medallion Architecture organizes data into bronze, silver and gold layers. (08:34) Task group challenges highlight the need for adaptable workflows. (15:04) Scaling managed services requires trial, error and tailored tweaks. (19:46) Terraform scales infrastructure, while YAML templates manage DAGs efficiently. (20:00) Templated DAGs and robust testing enhance platform management. (24:15) Open-source resources drive innovation in Airflow practices. Resources Mentioned: Arjun Anandkumar - https://www.linkedin.com/in/arjunanand1/?originalSubdomain=dk Telia - https://www.linkedin.com/company/teliacompany/ Apache Airflow - https://airflow.apache.org/ Cosmos by Astronomer - https://www.astronomer.io/cosmos/ Terraform - https://www.terraform.io/ Medallion Architecture by Databricks - https://www.databricks.com/glossary/medallion-architecture Thanks for listening to “The Data Flowcast: Mastering Airflow for Data Engineering & AI.” If you enjoyed this episode, please leave a 5-star review to help get the word out about the show. And be sure to subscribe so you never miss any of the insightful conversations. #AI #Automation #Airflow #MachineLearning…

The Data Flowcast: Mastering Airflow for Data Engineering & AI

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

cách đây 10 weeks trước21:19

21:19

Transforming bottlenecked finance processes into streamlined, automated systems requires the right tools and a forward-thinking approach. In this episode, Mihir Samant , Senior Data Analyst at Etraveli Group , joins us to share how his team leverages Airflow to revolutionize finance automation. With extensive experience in data workflows and a passion for open-source tools, Mihir provides valuable insights into building efficient, scalable systems. We explore the transformative power of Airflow in automating workflows and enhancing data orchestration within the finance domain. Key Takeaways: (02:14) Etraveli Group specializes in selling affordable flight tickets and ancillary services. (03:56) Mihir’s finance automation team uses Airflow to tackle month-end bottlenecks. (06:00) Airflow's flexibility enables end-to-end automation for finance workflows. (07:00) Open-source Airflow tools offer cost-effective solutions for new teams. (08:46) Sensors and dynamic DAGs are pivotal features for optimizing tasks. (13:30) GitSync simplifies development by syncing environments seamlessly. (16:27) Plans include integrating Databricks for more advanced data handling. (17:58) Airflow and Databricks offer multiple flexible methods to trigger workflows and execute SQL queries seamlessly. Resources Mentioned: Mihir Samant - https://www.linkedin.com/in/misamant/?originalSubdomain=ca Etraveli Group - https://www.linkedin.com/company/etraveli-group/ Apache Airflow - https://airflow.apache.org/ Docker - https://www.docker.com/ Databricks - https://www.databricks.com/ Thanks for listening to “The Data Flowcast: Mastering Airflow for Data Engineering & AI.” If you enjoyed this episode, please leave a 5-star review to help get the word out about the show. And be sure to subscribe so you never miss any of the insightful conversations. #AI #Automation #Airflow #MachineLearning…

The Data Flowcast: Mastering Airflow for Data Engineering & AI

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

cách đây 11 weeks trước38:54

38:54

Data engineering is entering a new era, where orchestration and automation are redefining how large-scale projects operate. This episode features Vasantha Kosuri-Marshall , Data and ML Ops Engineer at Ford Motor Company . Vasantha shares her expertise in managing complex data pipelines. She takes us through Ford's transition to cloud platforms, the adoption of Airflow and the intricate challenges of orchestrating data in a diverse environment. Key Takeaways: (03:10) Vasantha’s transition to the Advanced Driving Assist Systems team at Ford. (05:42) Early adoption of Airflow to orchestrate complex data pipelines. (09:29) Ford's move from on-premise data solutions to Google Cloud Platform. (12:03) The importance of Airflow's scheduling capabilities for efficient data management. (16:12) Using Kubernetes to scale Airflow for large-scale data processing. (19:59) Vasantha’s experience in overcoming challenges with legacy orchestration tools. (22:22) Integration of data engineering and data science pipelines at Ford. (28:03) How deferrable operators in Airflow improve performance and save costs. (32:12) Vasantha’s insights into tuning Airflow properties for thousands of DAGs. (36:09) The significance of monitoring and observability in managing Airflow instances. Resources Mentioned: Vasantha Kosuri-Marshall - https://www.linkedin.com/in/vasantha-kosuri-marshall-0b0aab188/ Apache Airflow - https://airflow.apache.org/ Google Cloud Platform (GCP) - https://cloud.google.com/ Ford Motor Company | LinkedIn - https://www.linkedin.com/company/ford-motor-company/ Ford Motor Company | Website - https://www.ford.com/ Astronomer - https://www.astronomer.io/ Thanks for listening to “The Data Flowcast: Mastering Airflow for Data Engineering & AI.” If you enjoyed this episode, please leave a 5-star review to help get the word out about the show. And be sure to subscribe so you never miss any of the insightful conversations. #AI #Automation #Airflow #MachineLearning…

The Data Flowcast: Mastering Airflow for Data Engineering & AI

1
Powering Finance With Advanced Data Solutions at Ramp with Ryan Delgado 24:35

cách đây 11 weeks trước24:35

24:35

Data is the backbone of every modern business, but unlocking its full potential requires the right tools and strategies. In this episode, Ryan Delgado , Director of Engineering at Ramp , joins us to explore how innovative data platforms can transform business operations and fuel growth. He shares insights on integrating Apache Airflow, optimizing data workflows and leveraging analytics to enhance customer experiences. Key Takeaways: (01:52) Data is the lifeblood of Ramp, touching every vertical in the company. (03:18) Ramp’s data platform team enables high-velocity scaling through tailored tools. (05:27) Airflow powers Ramp’s enterprise data warehouse integrations for advanced analytics. (07:55) Centralizing data in Snowflake simplifies storage and analytics pipelines. (12:08) Machine learning models at Ramp integrate seamlessly with Airflow for operational excellence. (14:11) Leveraging Airflow datasets eliminates inefficiencies in DAG dependencies. (17:22) Platforms evolve from solving narrow business problems to scaling organizationally. (18:55) ClickHouse enhances Ramp’s OLAP capabilities with 100x performance improvements. (19:47) Ramp’s OLAP platform improves performance by reducing joins and leveraging ClickHouse. (21:46) Ryan envisions a lighter-weight, more Python-native future for Airflow. Resources Mentioned: Ryan Delgado - https://www.linkedin.com/in/ryan-delgado-69544568/ Ramp - https://www.linkedin.com/company/ramp/ Apache Airflow - https://airflow.apache.org/ Snowflake - https://www.snowflake.com/ ClickHouse - https://clickhouse.com/ dbt - https://www.getdbt.com/ Thanks for listening to “The Data Flowcast: Mastering Airflow for Data Engineering & AI.” If you enjoyed this episode, please leave a 5-star review to help get the word out about the show. And be sure to subscribe so you never miss any of the insightful conversations. #AI #Automation #Airflow #MachineLearning…

The Data Flowcast: Mastering Airflow for Data Engineering & AI

1
Exploring the Power of Airflow 3 at Astronomer with Amogh Desai 30:24

cách đây 14 weeks trước30:24

30:24

What does it take to go from fixing a broken link to becoming a committer for one of the world’s leading open-source projects? Amogh Desai , Senior Software Engineer at Astronomer , takes us through his journey with Apache Airflow. From small contributions to building meaningful connections in the open-source community, Amogh’s story provides actionable insights for anyone on the cusp of their open-source journey. Key Takeaways: (02:09) Building data engineering platforms at Cloudera with Kubernetes. (04:00) Brainstorming led to contributing to Apache Airflow. (05:17) Starting small with link fixes, progressing to Breeze development. (07:00) Becoming a committer for Apache Airflow in September 2023. (09:51) The steep learning curve for contributing to Airflow. (16:30) Using GitHub’s “good-first-issue” label to get started. (18:15) Setting up a development environment with Breeze. (22:00) Open-source contributions enhance your resume and career. (24:51) Amogh’s advice: Start small and stay consistent. (28:12) Engage with the community via Slack, email lists and meetups. Resources Mentioned: Amogh Desai - https://www.linkedin.com/in/amogh-desai-385141157/?originalSubdomain=in%20%20https://www.linkedin.com/company/astronomer/ Astronomer - https://www.linkedin.com/company/astronomer/ Apache Airflow GitHub Repository - https://github.com/apache/airflow Contributors Quick Guide - https://github.com/apache/airflow/blob/main/CONTRIBUTING.rst Breeze Development Tool - https://github.com/apache/airflow/tree/main/dev/breeze Thanks for listening to “The Data Flowcast: Mastering Airflow for Data Engineering & AI.” If you enjoyed this episode, please leave a 5-star review to help get the word out about the show. And be sure to subscribe so you never miss any of the insightful conversations. #AI #Automation #Airflow #MachineLearning…

The Data Flowcast: Mastering Airflow for Data Engineering & AI

1
Using Airflow To Power Machine Learning Pipelines at Optimove with Vasyl Vasyuta 24:11

cách đây 16 weeks trước24:11

24:11

Data orchestration and machine learning are shaping how organizations handle massive datasets and drive customer-focused strategies. Tools like Apache Airflow are central to this transformation. In this episode, Vasyl Vasyuta , R&D Team Leader at Optimove , joins us to discuss how his team leverages Airflow to optimize data processing, orchestrate machine learning models and create personalized customer experiences. Key Takeaways: (01:59) Optimove tailors marketing notifications with personalized customer journeys. (04:25) Airflow orchestrates Snowflake procedures for massive datasets. (05:11) DAGs manage workflows with branching and replay plugins. (05:41) The "Joystick" plugin enables seamless data replays. (09:33) Airflow supports MLOps for customer data grouping. (11:15) Machine learning predicts customer behavior for better campaigns. (13:20) Thousands of DAGs run every five minutes for data processing. (15:36) Custom versioning allows rollbacks and gradual rollouts. (18:00) Airflow logs enhance operational observability. (23:00) DAG versioning in Airflow 3.0 could boost efficiency. Resources Mentioned: Vasyl Vasyuta - https://www.linkedin.com/in/vasyl-vasyuta-3270b54a/ Optimove - https://www.linkedin.com/company/optimove/ Apache Airflow - https://airflow.apache.org/ Snowflake - https://www.snowflake.com/ Datadog - https://www.datadoghq.com/ Apache Airflow Survey - https://astronomer.typeform.com/airflowsurvey24 Thanks for listening to “The Data Flowcast: Mastering Airflow for Data Engineering & AI.” If you enjoyed this episode, please leave a 5-star review to help get the word out about the show. And be sure to subscribe so you never miss any of the insightful conversations. #AI #Automation #Airflow #MachineLearning…

The Data Flowcast: Mastering Airflow for Data Engineering & AI

1
Maximizing Business Impact Through Data at GlossGenius with Katie Bauer 25:49

cách đây 17 weeks trước25:49

25:49

Bridging the gap between data teams and business priorities is essential for maximizing impact and building value-driven workflows. Katie Bauer , Senior Director of Data at GlossGenius , joins us to share her principles for creating effective, aligned data teams. In this episode, Katie draws from her experience at GlossGenius, Reddit and Twitter to highlight the common pitfalls data teams face and how to overcome them. She offers practical strategies for aligning team efforts with organizational goals and fostering collaboration with stakeholders. Key Takeaways: (02:36) GlossGenius provides an all-in-one platform for beauty professionals. (03:59) Airflow orchestrates data and MLOps workflows at GlossGenius. (04:41) Focusing on value helps data teams achieve greater impact. (06:23) Aligning team priorities with company goals minimizes friction. (08:44) Building strong stakeholder relationships requires curiosity. (12:46) Treating roles as flexible fosters team innovation. (13:21) Adapting to new technologies improves effectiveness. (18:28) Acting like your time is valuable earns respect. (23:38) Proactive data initiatives drive strategic value. (24:20) Usage data offers critical insights into tool effectiveness. Resources Mentioned: Katie Bauer - https://www.linkedin.com/in/mkatiebauer/ GlossGenius - https://www.linkedin.com/company/glossgenius/ Apache Airflow - https://airflow.apache.org/ DBT - https://www.getdbt.com/ Cosmos - https://cosmos.apache.org/ Apache Airflow Survey - https://astronomer.typeform.com/airflowsurvey24 Thanks for listening to “The Data Flowcast: Mastering Airflow for Data Engineering & AI.” If you enjoyed this episode, please leave a 5-star review to help get the word out about the show. And be sure to subscribe so you never miss any of the insightful conversations. #AI #Automation #Airflow #MachineLearning…

The Data Flowcast: Mastering Airflow for Data Engineering & AI

1
Optimizing Large-Scale Deployments at LinkedIn with Rahul Gade 27:47

cách đây 17 weeks trước27:47

27:47

Scaling deployments for a billion users demands innovation, precision and resilience. In this episode, we dive into how LinkedIn optimizes its continuous deployment process using Apache Airflow. Rahul Gade , Staff Software Engineer at LinkedIn , shares his insights on building scalable systems and democratizing deployments for over 10,000 engineers. Rahul discusses the challenges of managing large-scale deployments across 6,000 services and how his team leverages Airflow to enhance efficiency, reliability and user accessibility. Key Takeaways: (01:36) LinkedIn minimizes human involvement in production to reduce errors. (02:00) Airflow powers LinkedIn’s Continuous Deployment platform. (05:43) Continuous deployment adoption grew from 8% to a targeted 80%. (11:25) Kubernetes ensures scalability and flexibility for deployments. (12:04) A custom UI offers real-time deployment transparency. (16:23) No-code YAML workflows simplify deployment tasks. (17:18) Canaries and metrics ensure safe deployments across fabrics. (20:45) A gateway service ensures redundancy across Airflow clusters. (24:22) Abstractions let engineers focus on development, not logistics. (25:20) Multi-language support in Airflow 3.0 simplifies adoption. Resources Mentioned: Rahul Gade - https://www.linkedin.com/in/rahul-gade-68666818/ LinkedIn - https://www.linkedin.com/company/linkedin/ Apache Airflow - https://airflow.apache.org/ Kubernetes - https://kubernetes.io/ Open Policy Agent (OPA) - https://www.openpolicyagent.org/ Backstage - https://backstage.io/ Apache Airflow Survey - https://astronomer.typeform.com/airflowsurvey24 Thanks for listening to The Data Flowcast: Mastering Airflow for Data Engineering & AI. If you enjoyed this episode, please leave a 5-star review to help get the word out about the show. And be sure to subscribe so you never miss any of the insightful conversations. #AI #Automation #Airflow #MachineLearning…

The Data Flowcast: Mastering Airflow for Data Engineering & AI

1
How Uber Manages 1 Million Daily Tasks Using Airflow, with Shobhit Shah and Sumit Maheshwari 28:44

cách đây 20 weeks trước28:44

28:44

When data orchestration reaches Uber’s scale, innovation becomes a necessity, not a luxury. In this episode, we discuss the innovations behind Uber’s unique Airflow setup. With our guests Shobhit Shah and Sumit Maheshwari , both Staff Software Engineers at Uber , we explore how their team manages one of the largest data workflow systems in the world. Shobhit and Sumit walk us through the evolution of Uber’s Airflow implementation, detailing the custom solutions that support 200,000 daily pipelines. They discuss Uber's approach to tackling complex challenges in data orchestration, disaster recovery and scaling to meet the company’s extensive data needs. Key Takeaways: (02:03) Airflow as a service streamlines Uber’s data workflows. (06:16) Serialization boosts security and reduces errors. (10:05) Java-based scheduler improves system reliability. (13:40) Custom recovery model supports emergency pipeline switching. (15:58) No-code UI allows easy pipeline creation for non-coders. (18:12) Backfill feature enables historical data processing. (22:06) Regular updates keep Uber aligned with Airflow advancements. (26:07) Plans to leverage Airflow’s latest features. Resources Mentioned: Shobhit Shah - https://www.linkedin.com/in/shahshobhit/ Sumit Maheshwar - https://www.linkedin.com/in/maheshwarisumit/ Uber - https://www.linkedin.com/company/uber-com/ Apache Airflow - https://airflow.apache.org/ Airflow Summit - https://airflowsummit.org/ Uber - https://www.uber.com/tw/en/ Apache Airflow Survey - https://astronomer.typeform.com/airflowsurvey24 Thanks for listening to The Data Flowcast: Mastering Airflow for Data Engineering & AI. If you enjoyed this episode, please leave a 5-star review to help get the word out about the show. And be sure to subscribe so you never miss any of the insightful conversations. #AI #Automation #Airflow #MachineLearning…

The Data Flowcast: Mastering Airflow for Data Engineering & AI

1
Building Resilient Data Systems for Modern Enterprises at Astrafy with Andrea Bombino 28:29

cách đây 21 weeks trước28:29

28:29

Efficient data orchestration is the backbone of modern analytics and AI-driven workflows. Without the right tools, even the best data can fall short of its potential. In this episode, Andrea Bombino , Co-Founder and Head of Analytics Engineering at Astrafy , shares insights into his team’s approach to optimizing data transformation and orchestration using tools like datasets and Pub/Sub to drive real-time processing. Andrea explains how they leverage Apache Airflow and Google Cloud to power dynamic data workflows. Key Takeaways: (01:55) Astrafy helps companies manage data using Google Cloud. (04:36) Airflow is central to Astrafy’s data engineering efforts. (07:17) Datasets and Pub/Sub are used for real-time workflows. (09:59) Pub/Sub links multiple Airflow environments. (12:40) Datasets eliminate the need for constant monitoring. (15:22) Airflow updates have improved large-scale data operations. (18:03) New Airflow API features make dataset updates easier. (20:45) Real-time orchestration speeds up data processing for clients. (23:26) Pub/Sub enhances flexibility across cloud environments. (26:08) Future Airflow features will offer more control over data workflows. Resources Mentioned: Andrea Bombino - https://www.linkedin.com/in/andrea-bombino/ Astrafy - https://www.linkedin.com/company/astrafy/ Apache Airflow - https://airflow.apache.org/ Google Cloud - https://cloud.google.com/ dbt - https://www.getdbt.com/ Apache Airflow Survey - https://astronomer.typeform.com/airflowsurvey24 Thanks for listening to “The Data Flowcast: Mastering Airflow for Data Engineering & AI.” If you enjoyed this episode, please leave a 5-star review to help get the word out about the show. And be sure to subscribe so you never miss any of the insightful conversations. #AI #Automation #Airflow #MachineLearning…

The Data Flowcast: Mastering Airflow for Data Engineering & AI

1
Inside Airflow 3: Redefining Data Engineering with Vikram Koka 30:08

cách đây 22 weeks trước30:08

30:08

Data orchestration is evolving faster than ever and Apache Airflow 3 is set to revolutionize how enterprises handle complex workflows. In this episode, we dive into the exciting advancements with Vikram Koka , Chief Strategy Officer at Astronomer and PMC Member at The Apache Software Foundation . Vikram shares his insights on the evolution of Airflow and its pivotal role in shaping modern data-driven workflows, particularly with the upcoming release of Airflow 3. Key Takeaways: (02:36) Vikram leads Astronomer’s engineering and open-source teams for Airflow. (05:26) Airflow enables reliable data ingestion and curation. (08:17) Enterprises use Airflow for mission-critical data pipelines. (11:08) Airflow 3 introduces major architectural updates. (13:58) Multi-cloud and edge deployments are supported in Airflow 3. (16:49) Event-driven scheduling makes Airflow more dynamic. (19:40) Tasks in Airflow 3 can run in any language. (22:30) Multilingual task support is crucial for enterprises. (25:21) Data assets and event-based integration enhance orchestration. (28:12) Community feedback plays a vital role in Airflow 3. Resources Mentioned: Vikram Koka - https://www.linkedin.com/in/vikramkoka/ Astronomer - https://www.linkedin.com/company/astronomer/ The Apache Software Foundation LinkedIn - https://www.linkedin.com/company/the-apache-software-foundation/ Apache Airflow LinkedIn - https://www.linkedin.com/company/apache-airflow/ Apache Airflow - https://airflow.apache.org/ Astronomer - https://www.astronomer.io/ The Apache Software Foundation - https://www.apache.org/ Join the Airflow slack and/or Dev list - https://airflow.apache.org/community/ Apache Airflow Survey - https://astronomer.typeform.com/airflowsurvey24 Thanks for listening to The Data Flowcast: Mastering Airflow for Data Engineering & AI. If you enjoyed this episode, please leave a 5-star review to help get the word out about the show. And be sure to subscribe so you never miss any of the insightful conversations. #AI #Automation #Airflow #MachineLearning…

The Data Flowcast: Mastering Airflow for Data Engineering & AI

1
Building a Data-Driven HR Platform at 15Five with Guy Dassa 20:25

cách đây 23 weeks trước20:25

20:25

Data and AI are revolutionizing HR, empowering leaders to measure performance and drive strategic decisions like never before. In this episode, we explore the transformation of HR technology with Guy Dassa , Chief Technology Officer at 15Five , as he shares insights into their evolving data platform. Guy discusses how 15Five equips HR leaders with tools to measure and take action on team performance, engagement and retention. He explains their data-driven approach, highlighting how Apache Airflow supports their data ingestion, transformation, and AI integration. Key Takeaways: (01:54) 15Five acts as a command center for HR leaders. (03:40) Tools like performance reviews, engagement surveys, and an insights dashboard guide actionable HR steps. (05:33) Data visualization, insights, and action recommendations enhance HR effectiveness to improve their people's outcomes. (07:08) Strict data confidentiality and sanitized AI model training. (09:21) Airflow is central to data transformation and enrichment. (11:15) Airflow enrichment DAGs integrate AI models. (13:33) Integration of Airflow and DBT enables efficient data transformation. (15:28) Synchronization challenges arise with reverse ETL processes. (17:10) Future plans include deeper Airflow integration with AI. (19:31) Emphasizing the need for DAG versioning and improved dependency visibility. Resources Mentioned: Guy Dassa - https://www.linkedin.com/in/guydassa/ 15Five - https://www.linkedin.com/company/15five/ Apache Airflow - https://airflow.apache.org/ MLflow - https://mlflow.org/ DBT - https://www.getdbt.com/ Kubernetes - https://kubernetes.io/ RedShift - https://aws.amazon.com/redshift/ 15Five - https://www.15five.com/ Thanks for listening to The Data Flowcast: Mastering Airflow for Data Engineering & AI. If you enjoyed this episode, please leave a 5-star review to help get the word out about the show. And be sure to subscribe so you never miss any of the insightful conversations. #AI #Automation #Airflow #MachineLearning…

The Data Flowcast: Mastering Airflow for Data Engineering & AI

1
The Intersection of AI and Data Management at Dosu with Devin Stein 20:18

cách đây 25 weeks trước20:18

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…

The Data Flowcast: Mastering Airflow for Data Engineering & AI

1
AI-Powered Vehicle Automation at Ford Motor Company with Serjesh Sharma 26:11

cách đây 29 weeks trước26:11

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…

The Data Flowcast: Mastering Airflow for Data Engineering & AI

1
From Task Failures to Operational Excellence at GumGum with Brendan Frick 24:06

cách đây 30 weeks trước24:06

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…

The Data Flowcast: Mastering Airflow for Data Engineering & AI

1
From Sensors to Datasets: Enhancing Airflow at Astronomer with Maggie Stark and Marion Azoulai 22:25

cách đây 31 weeks trước22:25

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…

The Data Flowcast: Mastering Airflow for Data Engineering & AI

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

cách đây 31 weeks trước24:36

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…

The Data Flowcast: Mastering Airflow for Data Engineering & AI

1
Welcome to The Data Flowcast 2:01

cách đây 32 weeks trước2:01

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…

The Data Flowcast: Mastering Airflow for Data Engineering & AI

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

cách đây 33 weeks trước23:51

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…

The Data Flowcast: Mastering Airflow for Data Engineering & AI

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Cutting-Edge Data Engineering at Teya with Alexandre Magno Lima Martins 23:46

cách đây 34 weeks trước23:46

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…

The Data Flowcast: Mastering Airflow for Data Engineering & AI

1
Airflow Strategies for Business Efficiency at Campbell with Larry Komenda 26:10

cách đây 36 weeks trước26:10

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…

The Data Flowcast: Mastering Airflow for Data Engineering & AI

1
How Laurel Uses Airflow To Enhance Machine Learning Pipelines with Vincent La and Jim Howard 23:58

cách đây 37 weeks trước23:58

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…

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