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

https://airflow.apache.org/

Google Cloud

https://cloud.google.com/

Google Pub/Sub

https://cloud.google.com/pubsub

Google BigQuery

https://cloud.google.com/bigquery

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#AI #Automation #Airflow

Uphold’s Approach to Orchestrating Modern Data Workflows with Jaime Oliveira Feb 05, 2026

Show notes

A strong data-driven mindset underpins how fintech teams scale analytics, infrastructure and decision-making across the business.

In this episode, Jaime Oliveira, Lead Data Engineer at Uphold, joins us to discuss how Uphold structures its data organization and orchestration strategy. Jaime shares how the team uses Airflow and dbt to support analytics, reporting and data activation while evolving their approach as the stack grows.

Key Takeaways:

00:00 Introduction.

01:23 A data-driven mindset supports product development and business decisions.

02:55 Diverse ingestion pipelines enable scalable analytics.

04:18 A single orchestration platform simplifies analytics workflows.

05:17 Early experience with orchestration tools shapes engineering practices.

08:16 Analytics orchestration works best when aligned with transformation workflows.

09:25 Infrastructure choices involve tradeoffs in testing, visibility and overhead.

16:39 More collaborative workflow tools could improve accessibility and autonomy.

Resources Mentioned:

Jaime Oliveira

https://www.linkedin.com/in/jaime-oliveira-b075855a/

Uphold | LinkedIn

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

Uphold | Website

https://uphold.com

https://airflow.apache.org

dbt

https://www.getdbt.com

Snowflake

https://www.snowflake.com

https://kubernetes.io

Astronomer Cosmos

https://astronomer.github.io/astronomer-cosmos

Cosmos e-book

https://www.astronomer.io/ebooks/orchestrating-dbt-with-airflow-using-cosmos/

#AI #Automation #Airflow

Modern Airflow Best Practices for Scalable Data Pipelines with Bhavani Ravi Jan 29, 2026

Show notes

Building reliable data pipelines at scale requires more than writing code. It depends on thoughtful design, infrastructure trade-offs and an understanding of how orchestration platforms evolve over time.

In this episode, Airflow best practices shaped by real-world implementation are examined. Bhavani Ravi, Independent Software Consultant and Apache Airflow Champion, shares lessons on pipeline design, architectural decisions and the evolution of the Airflow ecosystem in modern data environments.

Key Takeaways:

00:00 Introduction.

01:30 Independent consulting supports effective Airflow adoption.

02:38 Early challenges shaped modern Airflow practices.

03:21 Airflow setup has become significantly simpler.

04:30 New features expanded workflow capabilities.

06:03 Frequent releases support long-term sustainability.

07:34 Community and providers strengthen the ecosystem.

10:03 Pipeline design should come before coding.

10:55 Decoupling logic requires careful trade-offs.

13:30 Plugins extend Airflow into new use cases.

Resources Mentioned:

Bhavani Ravi

https://www.linkedin.com/in/bhavanicodes/

https://airflow.apache.org/

https://kubernetes.io/

Azure Fabric

https://learn.microsoft.com/en-us/fabric/

#AI #Automation #Airflow

Inside Conviva’s Decision To Power Its Data Platform With Airflow with Han Zhang Jan 22, 2026

Show notes

Conviva operates at a massive scale, delivering outcome-based intelligence for digital businesses through real-time and batch data processing. As new use cases emerged, the team needed a way to extend a streaming-first architecture without rebuilding core systems.

In this episode, Han Zhang joins us to explain how Conviva uses Apache Airflow as the orchestration backbone for its batch workloads, how the control plane is designed and what trade-offs shaped their platform decisions.

Key Takeaways:

00:00 Introduction.

01:17 Large-scale data platforms require low-latency processing capabilities.

02:08 Batch workloads can complement streaming pipelines for additional use cases.

03:45 An orchestration framework can act as the core coordination layer.

06:12 Batch processing enables workloads that streaming alone cannot support.

08:50 Ecosystem maturity and observability are key orchestration considerations.

10:15 Built-in run history and logs make failures easier to diagnose.

14:20 Platform users can monitor workflows without managing orchestration logic.

17:08 Identity, secrets and scheduling present ongoing optimization challenges.

19:59 Configuration history and change visibility improve operational reliability.

Resources Mentioned:

Han Zhang

https://www.linkedin.com/in/zhanghan177

Conviva | Website

http://www.conviva.com

https://airflow.apache.org/

Celery

https://docs.celeryq.dev/

Temporal

https://temporal.io/

https://kubernetes.io/

LDAP

https://ldap.com/

#AI #Automation #Airflow

Why Airflow Became the Scheduling Backbone at Condé Nast Technology Lab with Arun Karthik Jan 15, 2026

Show notes

Data platforms are moving from batch-first pipelines to near real-time systems where orchestration, observability, scalability and governance all have to work together.

In this episode, Arun Karthik, Director, Data Solutions Engineering at Condé Nast Technology Lab, joins us to share how data engineering evolves from relational databases and ETL into distributed processing, modern orchestration with Apache Airflow and managed Airflow with Astronomer.

Key Takeaways:

00:00 Introduction.

02:13 Early data systems rely heavily on relational databases and batch-oriented processing models.

07:01 Scheduling requirements evolve beyond fixed time windows as dependencies increase.

10:14 Ease of use and developer experience influence adoption of orchestration frameworks.

13:22 Operating open source orchestration tools requires ongoing engineering effort.

14:45 Managed services help teams reduce infrastructure and maintenance responsibilities.

17:27 Observability improves confidence in pipeline execution and system health.

19:12 Governance considerations grow in importance as data platforms mature.

20:46 Building data systems requires balancing speed, reliability and long-term sustainability.

Resources Mentioned:

Arun Karthik

https://www.linkedin.com/in/earunkarthik/

Condé Nast Technology Lab | LinkedIn

https://www.linkedin.com/company/conde-nast-technology-lab/

Condé Nast Technology Lab | Website

https://www.condenast.com/

https://airflow.apache.org/

https://www.astronomer.io/

Apache Spark

https://spark.apache.org/

Apache Hadoop

https://hadoop.apache.org/

Jenkins

https://www.jenkins.io/

dbt Labs

https://www.getdbt.com/product/what-is-dbt

Amazon Web Services

https://aws.amazon.com/free/?trk=54026797-7540-48d8-9f6b-0db2c3a0040c&sc_channel=ps&trk=54026797-7540-48d8-9f6b-0db2c3a0040c&sc_channel=ps&ef_id=CjwKCAiAmp3LBhAkEiwAJM2JUKIc3E2I-hDlF6fRWgZn5n2-RWX-kEDAVApJYd88wwlsiyosV71VixoCmRoQAvD_BwE:G:s&s_kwcid=AL!4422!3!785574063524!e!!g!!amazon%20web%20services!23291338728!189486861095&gad_campaignid=23291338728&gbraid=0AAAAADjHtp813XNbg7azDj5QMwJPbGNqZ&gclid=CjwKCAiAmp3LBhAkEiwAJM2JUKIc3E2I-hDlF6fRWgZn5n2-RWX-kEDAVApJYd88wwlsiyosV71VixoCmRoQAvD_BwE

#AI #Automation #Airflow

The Role of Airflow in Building Smarter ML Pipelines at Vivian Health with Max Calehuff Dec 11, 2025

Show notes

The integration of data orchestration and machine learning is critical to operational efficiency in healthcare tech. Vivian Health leverages Airflow to power both its ETL pipelines and ML workflows while maintaining strict compliance standards.

Max Calehuff, Lead Data Engineer at Vivian Health, joins us to discuss how his team uses Airflow for ML ops, regulatory compliance and large-scale data orchestration. He also shares insights into upgrading to Airflow 3 and the importance of balancing flexibility with security in a healthcare environment.

Key Takeaways:

00:00 Introduction.

04:21 The role of Airflow in managing ETL pipelines and ML retraining.

06:23 Using AWS SageMaker for ML training and deployment.

07:47 Why Airflow’s versatility makes it ideal for MLOps.

10:50 The importance of documentation and best practices for engineering teams.

13:44 Automating anonymization of user data for compliance.

15:30 The benefits of remote execution in Airflow 3 for regulated industries.

18:16 Quality-of-life improvements and desired features in future Airflow versions.

Resources Mentioned:

Max Calehuff

https://www.linkedin.com/in/maxwell-calehuff/

Vivian Health | LinkedIn

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

Vivian Health | Website

https://www.vivian.com

https://airflow.apache.org/

https://www.astronomer.io/

AWS SageMaker

https://www.google.com/aclk?sa=L&ai=DChsSEwj3-fbz1tiQAxWXlKYDHXUBBVoYACICCAEQABoCdGI&ae=2&aspm=1&co=1&ase=2&gclid=Cj0KCQiA5abIBhCaARIsAM3-zFWbfj2olUvX4dqoiYNaE3q2fMf_ZifRjmbKNQCVX7D6ZMClaUXUkFkaAuwmEALw_wcB&cid=CAASQuRoMccxWhBvMq-1Uez3XOZti1ul7mTDotKvSMoDHv0q2xCsyS2FzMptO5dJf3tmfkLRu22TtD8ChTmdjvs6YetTjQ&cce=2&category=acrcp_v1_35&sig=AOD64_2xE2xolEEVbpDb56qXQluxTzs-Aw&q&nis=4&adurl&ved=2ahUKEwj7le3z1tiQAxWXcvUHHfZePbAQ0Qx6BAgUEAE

dbtLabs

https://www.getdbt.com/

Cosmos

https://github.com/astronomer/astronomer-cosmos

Split

https://www.split.io/

Snowflake

https://www.snowflake.com/en/

#AI #Automation #Airflow

Scaling Airflow to 11,000 DAGs Across Three Regions at Intercom with András Gombosi and Paul Vickers Dec 04, 2025

Show notes

The evolution of Intercom’s data infrastructure reveals how a well-built orchestration system can scale to serve global needs. With thousands of DAGs powering analytics, AI and customer operations, the team’s approach combines technical depth with organizational insight.

In this episode, András Gombosi, Senior Engineering Manager of Data Infra and Analytics Engineering, and Paul Vickers, Principal Engineer, both at Intercom, share how they built one of the largest Airflow deployments in production and enabled self-serve data platforms across teams.

Key Takeaways:

00:00 Introduction.

04:24 Community input encourages confident adoption of a common platform.

08:50 Self-serve workflows require consistent guardrails and review.

09:25 Internal infrastructure support accelerates scalable deployments.

13:26 Batch LLM processing benefits from a configuration-driven design.

15:20 Standardized development environments enable effective AI-assisted work.

19:58 Applied AI enhances internal analysis and operational enablement.

27:27 Strong test coverage and staged upgrades protect stability.

30:36 Proactive observability and on-call ownership improve outcomes.

Resources Mentioned:

András Gombosi

https://www.linkedin.com/in/andrasgombosi/

Paul Vickers

https://www.linkedin.com/in/paul-vickers-a22b76a3/

Intercom | LinkedIn

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

Intercom | Website

https://www.intercom.com

https://airflow.apache.org/

dbtLabs

https://www.getdbt.com/

Snowflake Cortex AI

https://www.snowflake.com/en/product/features/cortex/

Datadog

https://www.datadoghq.com/

#AI #Automation #Airflow

How Covestro Turns Airflow Into a Simulation Toolbox with Anja Mackenzie Nov 20, 2025

Show notes

Building scalable, reproducible workflows for scientific computing often requires bridging the gap between research flexibility and enterprise reliability.

In this episode, Anja MacKenzie, Expert for Cheminformatics at Covestro, explains how her team uses Airflow and Kubernetes to create a shared, self-service platform for computational chemistry.

Key Takeaways:

00:00 Introduction.

06:19 Custom scripts made sharing and reuse difficult.

09:29 Workflows are manually triggered with user traceability.

10:38 Customization supports varied compute requirements.

12:48 Persistent volumes allow tasks to share large amounts of data.

14:25 Custom operators separate logic from infrastructure.

16:43 Modified triggers connect dependent workflows.

18:36 UI plugins enable file uploads and secure access.

Resources Mentioned:

Anja MacKenzie

https://www.linkedin.com/in/anja-mackenzie/

Covestro | LinkedIn

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

Covestro | Website

https://www.covestro.com

https://airflow.apache.org/

Airflow KubernetesPodOperator

https://kubernetes.io/

https://airflow.apache.org/docs/apache-airflow-providers-cncf-kubernetes/stable/operators.html

Airflow Academy by Marc Lamberti

https://www.astronomer.io/

https://www.udemy.com/user/lockgfg/?utm_source=adwords&utm_medium=udemyads&utm_campaign=Search_DSA_GammaCatchall_NonP_la.EN_cc.ROW-English&campaigntype=Search&portfolio=ROW-English&language=EN&product=Course&test=&audience=DSA&topic=&priority=Gamma&utm_content=deal4584&utm_term=_._ag_169801645584_._ad_700876640602_._kw__._de_c_._dm__._pl__._ti_dsa-1456167871416_._li_9061346_._pd__._&matchtype=&gad_source=1&gad_campaignid=21341313808&gbraid=0AAAAADROdO1_-I2TMcVyU8F3i1jRXJ24K&gclid=Cj0KCQjwvJHIBhCgARIsAEQnWlC1uYHIRm3y9Q8rPNSuVPNivsxogqfczpKHwhmNho2uKZYC-y0taNQaApU2EALw_wcB

Airflow Documentation

https://airflow.apache.org/docs/

Airflow Plugins

https://airflow.apache.org/docs/apache-airflow/1.10.9/plugins.html

#AI #Automation #Airflow

Building Secure Financial Data Platforms at AgileEngine with Valentyn Druzhynin Nov 13, 2025

Show notes

The use of Apache Airflow in financial services demands a balance between innovation and compliance. Agile Engine’s approach to orchestration showcases how secure, auditable workflows can scale even within the constraints of regulatory environments.

In this episode, Valentyn Druzhynin, Senior Data Engineer at AgileEngine, discusses how his team leverages Airflow for ETF calculations, data validation and workflow reliability within tightly controlled release cycles.

Key Takeaways:

00:00 Introduction.

03:24 The orchestrator ensures secure and auditable workflows.

05:13 Validations before and after computation prevent errors.

08:24 Release freezes shape prioritization and delivery plans.

11:14 Migration plans must respect managed service constraints.

13:04 Versioning, backfills and event triggers increase reliability.

15:08 UI and integration improvements simplify operations.

18:05 New contributors should start small and seek help.

Resources Mentioned:

Valentyn Druzhynin

https://www.linkedin.com/in/valentyn-druzhynin/

AgileEngine | LinkedIn

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

AgileEngine | Website

https://agileengine.com/

https://airflow.apache.org/