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/

Apache Airflow

https://airflow.apache.org/

Kubernetes

https://kubernetes.io/

Azure Fabric

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

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

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

Apache Airflow

https://airflow.apache.org/

Celery

https://docs.celeryq.dev/

Temporal

https://temporal.io/

Kubernetes

https://kubernetes.io/

LDAP

https://ldap.com/

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

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/

Apache Airflow

https://airflow.apache.org/

Astronomer

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

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

Clean financial reporting depends on orchestration that catches issues early, prevents bad data from spreading and helps teams react fast when something breaks.

In this episode, Ayush Pradhan, Senior Analytics Engineer at Snowflake, joins us to explain how Snowflake’s finance data team relies on @Apache Airflow, sensors and dbt to keep revenue, cost and accounting pipelines accurate.

Key Takeaways:

00:00 Introduction.

02:25 Airflow is used to coordinate recurring finance workflows.

04:20 Sensors enforce the timing for the end of the measurement period

06:50 Anomaly checks help catch issues near the source.

08:30 Alerts route to owners quickly through common channels.

10:38 Failed quality checks block downstream publishing.

11:50 New features are adopted only when tied to business value.

15:42 Vendor sensors can trigger workflows after upstream updates.

16:52 Community learnings help teams keep pace with open-source change.

Resources Mentioned:

Ayush Pradhan

https://www.linkedin.com/in/ayush-pradhan-845a19194/

Snowflake | LinkedIn

https://www.linkedin.com/company/snowflake-computing/

Snowflake | Website

https://www.snowflake.com

Apache Airflow

https://airflow.apache.org/

dbt Labs

https://www.getdbt.com/

Slack

https://slack.com/

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

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

Apache Airflow

https://airflow.apache.org/

Astronomer

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/

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

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

Apache Airflow

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/

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

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

Apache Airflow

https://airflow.apache.org/

Kubernetes

https://kubernetes.io/

Airflow KubernetesPodOperator

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

Astronomer

https://www.astronomer.io/

Airflow Academy by Marc Lamberti

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

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

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/

Apache Airflow

https://airflow.apache.org/

Astronomer

https://www.astronomer.io/

AWS Managed Airflow

https://aws.amazon.com/managed-workflows-for-apache-airflow/

Google Cloud Composer (Managed Airflow)

https://cloud.google.com/composer

Airflow Summit

https://airflowsummit.org/

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

The life sciences industry relies on data accuracy, regulatory insight and quality intelligence. Building a unified system that keeps these elements aligned is no small feat.

In this episode, we welcome Shankar Mahindar, Senior Data Engineer II at Redica Systems. We discuss how the team restructures its data platform with Airflow to strengthen governance, reduce compliance risk and improve customer experience.

Key Takeaways:

00:00 Introduction.

01:53 A focused analytics platform reduces compliance risk in life sciences.

07:31 A centralized warehouse orchestrated by Airflow strengthens governance.

09:12 Managed orchestration keeps attention on analytics and outcomes.

10:32 A modern transformation stack enables scalable modeling and operations.

11:51 Event-driven pipelines improve data freshness and responsiveness.

14:13 Asset-oriented scheduling and versioning enhance reliability and change control.

16:53 Observability and SLAs build confidence in data quality and freshness.

21:04 Priorities include partitioned assets and streamlined developer tooling.

Resources Mentioned:

Shankar Mahindar

https://www.linkedin.com/in/shankar-mahindar-83a61b137/

Redica Systems | LinkedIn

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

Redica Systems | Website

https://redica.com

Apache Airflow

https://airflow.apache.org/

Astronomer

https://www.astronomer.io/

Snowflake

https://www.snowflake.com/

AWS

https://aws.amazon.com/

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

The Financial Times leverages Airflow and AI to uncover powerful stories hidden within vast, unstructured data.

In this episode, Zdravko Hvarlingov, Senior Software Engineer at the Financial Times, discusses building multi-tenant Airflow systems and AI-driven pipelines that surface stories that might otherwise be missed. Zdravko walks through entity extraction and fuzzy matching, linking the UK Register of Members’ Financial Interests with Companies House, and how this work cuts weeks of manual analysis to minutes.

Key Takeaways:

00:00 Introduction.

02:12 What computational journalism means for day-to-day newsroom work.

05:22 Why a shared orchestration platform supports consistent, scalable workflows.

08:30 Tradeoffs of one centralized platform versus many separate instances.

11:52 Using pipelines to structure messy sources for faster analysis.

14:14 Turning recurring disclosures into usable data for investigations.

16:03 Applying lightweight ML and matching to reveal entities and links.

18:46 How automation reduces manual effort and shortens time to insight.

20:41 Practical improvements that make backfilling and reliability easier.

Resources Mentioned:

Zdravko Hvarlingov

https://www.linkedin.com/in/zdravko-hvarlingov-3aa36016b/

Financial Times | LinkedIn

https://www.linkedin.com/company/financial-times/

Financial Times | Website

https://www.ft.com/

Apache Airflow

https://airflow.apache.org/

UK Register of Members’ Financial Interests

https://www.parliament.uk/mps-lords-and-offices/standards-and-financial-interests/parliamentary-commissioner-for-standards/registers-of-interests/register-of-members-financial-interests/

UK Companies House

https://www.gov.uk/government/organisations/companies-house

Doppler

https://www.doppler.com/

Kubernetes

https://kubernetes.io/

Airflow Kubernetes Executor

https://airflow.apache.org/docs/apache-airflow/stable/executor/kubernetes.html

GitHub

https://github.com/

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