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

Latest Episodes:

Streamlining AI and ML Operations at IBM with BJ Adesoji and Ryan Yackel Jun 05, 2025

Show notes

The orchestration layer is foundational to building robust AI- and ML-powered data pipelines, especially in complex hybrid enterprise environments. IBM’s partnership with Astronomer reflects a strategic alignment to simplify and scale Airflow-based workflows across industries.

In this episode, we’re joined by IBM’s Senior Product Manager, BJ Adesoji, and GTM PM and Growth Leader, Ryan Yackel. We discuss how IBM customers are using Airflow in production, the challenges they face at scale and what the new IBM–Astronomer collaboration unlocks.

Key Takeaways:

(03:09) The growing importance of orchestration tools in enterprise environments.

(04:48) How organizations are expanding orchestration beyond traditional use cases.

(05:24) Common patterns across industries adopting orchestration platforms.

(07:16) Why orchestration is essential for supporting business-critical workloads.

(10:00) The role of orchestration in compliance and regulatory processes.

(13:02) Challenges enterprises face when managing orchestration infrastructure.

(14:58) Opportunities to simplify and centralize orchestration at scale.

(19:11) The value of integrating orchestration with broader data toolchains.

(20:54) How AI is shaping the future of orchestrated data workflows.

Resources Mentioned:

BJ 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

Inside the Custom Framework for Managing Airflow Code at Wix with Gil Reich May 29, 2025

Show notes

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/

#AI #Automation #Airflow #MachineLearning

Modernizing Legacy Data Systems With Airflow at Procter & Gamble with Adonis Castillo Cordero May 22, 2025

Show notes

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/

#AI #Automation #Airflow #MachineLearning

Building an End-to-End Data Observability System at Netflix with Joseph Machado May 15, 2025

Show notes

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/

#AI #Automation #Airflow #MachineLearning

Why Developer Experience Shapes Data Pipeline Standards at Next Insurance with Snir Israeli May 08, 2025

Show notes

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/

#AI #Automation #Airflow #MachineLearning

Data Quality and Observability at Tekmetric with Ipsa Trivedi May 01, 2025

Show notes

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/

#AI #Automation #Airflow #MachineLearning

Introducing Apache Airflow® 3 with Vikram Koka and Jed Cunningham Apr 24, 2025

Show notes

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/

#AI #Automation #Airflow #MachineLearning

Airflow in Action: Powering Instacart's Complex Ecosystem Apr 17, 2025

Show notes

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

From ETL to Airflow: Transforming Data Engineering at Deloitte Digital with Raviteja Tholupunoori Apr 10, 2025

Show notes

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/

#AI #Automation #Airflow #MachineLearning

A Deep Dive Into the 2025 State of Airflow Survey Results with Tamara Fingerlin of Astronomer Apr 03, 2025

Show notes

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