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

Astronomer AI Skills Repo

http://www.astronomer.io/cosmos

http://www.github.com/astronomer/airflow-llm-providers-demo

Datadog

http://www.datadoghq.com

#AI #Automation #Airflow #MachineLearning

Building Airflow CTL with Buğra Öztürk at Mollie

support@contentallies.com (Astronomer) — Thu, 30 Apr 2026 00:00:00 -0400

Buğra Öztürk, Senior Data Engineer at Mollie and Committer and PMC member on the Apache Airflow project, joins us to walk through Airflow CTL — what it is, how it differs from the existing Airflow CLI and where it is headed under AIP-94.

Key Takeaways:

00:00 Introduction.

03:10 Buğra has contributed to Airflow since 2022, from docs changes up to Committer and PMC member — a path he hopes inspires others to start small and contribute.

04:05 Airflow CTL solves secure user interaction by abstracting database credentials behind the public core API.

05:13 Airflow CLI and Airflow CTL are complementary — CLI handles administration and database management while CTL handles secure user interactions via the API.

07:08 Airflow CTL authenticates via the API, acquires a JWT token and stores it securely in the OS keyring — running on the user's machine and never requiring direct database access.

08:21 Concrete use cases include local DAG development without the UI and CI/CD automation using headless mode with short-lived JWT tokens.

10:08 AIP-94 describes the long-term vision — decoupling all remote commands from the Airflow CLI and routing them through Airflow CTL.

13:12 Airflow CTL is currently at 0.X and already being used in CI and deployment automations. The move to 1.0 with full CLI parity is the next milestone under AIP-94.

16:09 Multi-team deployment becoming generally available in a future Airflow release is Buğra's most-anticipated upcoming feature beyond Airflow CTL.

Resources Mentioned:

Buğra Öztürk

https://www.linkedin.com/in/bugraozturk93/

Mollie

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

Mollie | Website

https://www.mollie.com/

Apache Airflow CTL

https://airflow.apache.org/

AIP-94 on Airflow Confluence

https://lists.apache.org/thread/d2o1pr78wxdp1wozq519stp0pkcv6k6c

Apache Airflow GitHub

https://www.github.com/apache/airflow

#AI #Automation #Airflow #MachineLearning

Introducing Airflow’s Common AI Provider with Pavan Kumar Gopidesu and Kaxil Naik

support@contentallies.com (Astronomer) — Thu, 23 Apr 2026 10:25:00 -0400

In this episode, we explore the newly released Apache Airflow common AI provider — what problem it solves, how it was built and what's coming next.

Kaxil Naik, Senior Director of Engineering at Astronomer and Apache Airflow PMC member, and Pavan Kumar Gopidesu, Lead Data Engineer at Experian and Apache Airflow PMC member, join us to walk through the provider's first release and the technical decisions behind it.

Key Takeaways:

00:00 Introduction.

04:05 The common AI provider was born from a real production problem.

07:10 Airflow already had the primitives needed for durable agent execution, making it the natural foundation for AI orchestration.

09:15 The LLM schema compare operator uses Apache DataFusion to fetch source schemas.

11:07 Apache DataFusion was chosen for its speed.

13:09 Hook tool sets expose Airflow's provider hooks to agents with an allowed methods list that blocks destructive operations.

15:20 Passing durable=True to an LLM operator caches tool calls and LLM outputs mid-task.

18:13 The provider offers three abstraction levels.

21:20 The provider currently requires Airflow 3 — the team is open to adding Airflow 2.11 support if demand is high enough.

24:10 MCP server configs can be stored as Airflow connections.

Resources Mentioned:

Kaxil Naik

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

Pavan Kumar Gopidesu

https://www.linkedin.com/in/pavan-kumar-gopidesu/

Astronomer | LinkedIn

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

Astronomer | Website

https://www.astronomer.io

Experian

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

Apache Airflow common AI provider docs

https://www.linkedin.com/company/apache-airflow

https://airflow.apache.org/docs/apache-airflow-providers-common-ai/stable/commits.html

Apache DataFusion

https://datafusion.apache.org/

Pydantic AI

https://pydantic.dev/docs/ai/overview/

Airflow Slack

https://airflow.apache.org/docs/apache-airflow-providers-slack/stable/index.html

Introducing the Common AI Provider: LLM and AI Agent Support for Apache Airflow

https://airflow.apache.org/blog/common-ai-provider/

#Automation #Airflow #MachineLearning

Building AI Debugging Agents Into Airflow DAGs at Jeppesen ForeFlight with Samantha Blaney Cuevas

support@contentallies.com (Astronomer) — Thu, 16 Apr 2026 00:00:00 -0400

Aviation data pipelines run on strict 28-day publication cycles, and the margin for error is zero. In this episode, we're joined by Samantha Blaney Cuevas, Software Engineer at Jeppesen ForeFlight, to explore how her team orchestrates a complex, time-sensitive data pipeline with Airflow and where AI is starting to fit into that picture.

Key Takeaways:

00:00 Introduction.

04:05 Airflow orchestrates almost all business logic and data transformations across the cycle, with custom timetables built to track busy and slow periods programmatically.

06:10 Cycle-aware sensing tasks handle irregular source deliveries, including duplicates and early or late arrivals, without disrupting the pipeline.

08:07 The two main AI use cases are pipeline debugging and cycle awareness — both designed to reduce the manual overhead of monitoring a complex DAG dependency graph.

09:03 The Data Port agent is a two-task DAG that routes Slack pipeline alerts to either a predefined command list or an AI token, depending on whether the fix is already known.

13:10 AI is still in development at Jeppesen ForeFlight — the team is focused on token efficiency and scoping how much autonomy to give agents across different environments.

15:04 Airflow setup and MCP configuration were straightforward — the harder design work was deciding which environments agents could access across QA staging and production.

17:06 Airflow's skills repo and agent tooling are helping onboard new developers and extend pipeline awareness to analysts who work alongside engineers on the cycle.

19:10 Samantha would like to see single-task retries with different parameters in Airflow — resetting one task without clearing the full pipeline run.

21:05 A future AI use case under consideration is live DAG editing and re-upload within Airflow to make one-off fixes without halting pipeline progress.

Resources Mentioned:

Samantha Blaney Cuevas

https://www.linkedin.com/in/samantha-blaney/

Jeppesen ForeFlight | LinkedIn

https://www.linkedin.com/company/jeppesen-foreflight/

Jeppesen ForeFlight | Website

http://www.foreflight.com

Astronomer Airflow Skills Repo

http://www.github.com/astronomer/airflow-llm-providers-demo

https://airflow.apache.org/

#AI #Automation #Airflow

Introducing Airflow 3.2

support@contentallies.com (Astronomer) — Thu, 09 Apr 2026 00:00:00 -0400

We introduce Airflow 3.2 and its updates for teams that build and operate data pipelines.

Astronomer’s Head of Customer Education, Marc Lamberti, and Senior Manager of Developer Relations, Kenten Danas, break down what’s new, from asset partitioning to Async Python tasks and DAG versioning. They explore how these updates improve scheduling, performance and observability in production workflows.

Key Takeaways:

00:00 Introduction.

02:10 Airflow 3 architecture separates workers from the metadata database.

03:05 Plugin versioning and UI-based backfills simplify operations.

06:20 Asset partitioning enables granular, partition-level scheduling.

07:15 Triggering DAGs on partitions instead of full datasets.

11:05 Deferrable operators reduce worker slot usage.

12:00 Async operators reduce database pressure and overhead.

14:10 Async improves throughput, not single task speed.

22:20 Inlets and outlets improve asset lineage visibility.

23:00 DAG version markers show changes directly in the UI.

Resources Mentioned:

Marc Lamberti

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

Asynchronous Processes Guide

https://airflow.apache.org/

Astronomer | LinkedIn

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

Astronomer | Website

https://www.astronomer.io/

3.2 Webinar

https://www.astronomer.io/events/webinars/introducing-airflow-3-2-video

Asset Partitioning Guide

https://www.astronomer.io/docs/learn/airflow-partitioned-runs

https://www.astronomer.io/docs/learn/deferrable-operators

Release Notes

https://airflow.apache.org/docs/apache-airflow/stable/release_notes.html#airflow-3-2-0-2026-04-07

Provider Registry

https://airflow.apache.org/registry/

#AI #Automation #Airflow #MachineLearning

Reflections on a Decade of Data Engineering at Seattle Data Guy

support@contentallies.com (Astronomer) — Fri, 03 Apr 2026 02:06:00 -0400

Lessons from the past decade of data engineering reveal how much the ecosystem has changed and what has stayed surprisingly consistent.

In this episode, Benjamin Rogojan, Owner and Data Consultant at Seattle Data Guy, joins us to reflect on how the data engineering landscape has evolved alongside Apache Airflow. We explore when Airflow makes sense as an orchestrator, why batch processing is still dominant and how AI is reshaping the workflows and responsibilities of modern data engineers.

Key Takeaways:

00:00 Introduction.

03:00 Airflow becomes valuable when workflows involve many pipelines, teams and dependencies.

05:00 Data engineers are still focused on making data accessible and aligning work with business needs.

05:30 Batch pipelines remain the most common approach even as real-time use cases grow.

07:45 Many “real-time” requests are actually event-driven batch workflows.

09:00 Airflow replaced many custom-built pipeline systems with built-in dependency management.

11:00 Modern orchestration tools often build on Airflow concepts or differentiate from them.

14:00 AI can assist with writing SQL and pipelines but still requires experienced engineers.

15:30 Organizations are collecting increasingly granular data creating more engineering demand.

19:00 The data stack has shifted rapidly from Hadoop-era systems to modern cloud platforms.

Resources Mentioned:

Benjamin Rogojan

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

Seattle Data Guy

https://www.linkedin.com/company/seattle-data-guy/

Airflow Summit / Airflow Conference

https://airflow.apache.org

https://airflowsummit.org

HubSpot Data Sharing / APIs

https://www.snowflake.com

https://developers.hubspot.com

MLflow

https://mlflow.org

#AI #Automation #Airflow

Managing Data Quality and Governance With Airflow at Credit Karma with Ashir Alam

support@contentallies.com (Astronomer) — Thu, 26 Mar 2026 05:33:00 -0400

Data quality is not optional when you manage credit data at scale.

In this episode, Ashir Alam, Senior Data Engineer at Credit Karma, joins us to share how his team acts as the gatekeeper for credit data ingestion, how they standardize data quality with Airflow and DAG Factory and how they scale safely across thousands of DAGs. We explore how governance, PII protection and orchestration come together inside a modern data platform.

Key Takeaways:

00:00 Introduction.

01:00 Overview of Credit Karma’s products and financial data ecosystem.

02:00 The team acts as gatekeepers for ingesting data from TransUnion and Equifax.

03:00 Why PII handling and controlled downstream access led to adopting Airflow.

04:00 BigQuery as the warehouse and Airflow as the primary orchestrator.

05:00 Why data quality and governance are critical in financial systems.

07:00 Why Airflow was selected: ease of use and unified ETL plus data quality.

09:00 Introduction to DAG Factory and YAML-based DAG generation.

10:00 GitHub executor creates PR-driven DAG workflows with CI checks.

12:00 BigQuery operators, structured checks and custom Slack and PagerDuty alerts.

13:00 Failed checks stop ETL pipelines and trigger notifications.

17:00 Scaling DAG Factory across thousands of DAGs and runtime vs compile-time concerns.

19:00 Future improvements: better defaults, retries and GenAI workflows in Airflow.

Resources Mentioned:

Ashir Alam

https://www.linkedin.com/in/ashir-alam/

Credit Karma

https://www.linkedin.com/company/intuit-credit-karma/

https://airflow.apache.org/

DAG Factory

https://github.com/astronomer/dag-factory

BigQuery (Google Cloud)

https://cloud.google.com/bigquery

GitHub

https://github.com/

Slack

https://slack.com/

PagerDuty

https://www.pagerduty.com/

#AI #Automation #Airflow

Open Source Airflow Contributions and Performance Improvements at G-Research with Christos Bisias

support@contentallies.com (Astronomer) — Thu, 19 Mar 2026 00:00:00 -0400

Modern Airflow isn’t just orchestration. It's a contribution.

In this episode, we explore how open source investment drives real performance gains and deeper observability.

We’re joined by Christos Bisias, Open Source Software Engineer, Apache Airflow at G-Research, to discuss how his team uses Airflow for large-scale data transformations, contributes upstream and improves scheduler throughput and OpenTelemetry support. From trace-level observability to CI-enforced metrics governance and a major scheduler optimization, this conversation spans strategy, engineering and community impact.

Key Takeaways:

00:00 Introduction.

01:20 How G-Research applies machine learning and big data to predict financial market movements.

02:15 Contributing to open source is a business decision.

03:10 Maintaining a fork is costly.

04:30 OpenTelemetry collects metrics, logs and traces to provide deep system visibility.

06:10 Custom spans help identify bottlenecks inside tasks and enable performance optimization.

08:05 OpenTelemetry integration works properly in Airflow 3.0 and above.

10:00 A YAML-based metrics registry with CI enforcement ensures consistency between docs and exported metrics.

12:10 Scheduler throughput improved significantly by applying concurrency limits earlier in the database query.

15:20 Future Task SDK changes may enable language-agnostic DAG authoring beyond Python.

Resources Mentioned:

Christos Bisias

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

G-Research

https://www.linkedin.com/company/g-research/

https://airflow.apache.org/

OpenTelemetry

https://opentelemetry.io/

Prometheus

https://prometheus.io/

Google Cloud Composer (Managed Airflow on GCP)

https://grafana.com/

Jaeger

https://www.jaegertracing.io/

#AI #Automation #Airflow

Automating Threat Intelligence Using Airflow with Karan Alang

support@contentallies.com (Astronomer) — Thu, 12 Mar 2026 02:58:00 -0400

In this episode, Karan Alang, Principal Software Engineer at Versa Networks, joins the conversation to discuss how Airflow can be used to automate threat intelligence in modern cybersecurity environments. He explains the growing scale of cloud computing, the profitability of hacking and the shortage of SOC analysts. Karan also outlines a novel architecture that combines Airflow, XDR, graph databases and LLMs to orchestrate automated threat detection and response.

Key Takeaways:

00:00 Introduction.

05:00 Organizations face massive log volumes and a shortage of SOC analysts.

07:00 The solution integrates Airflow, XDR, Neo4j graph databases and LLMs into one architecture.

08:00 MITRE ATT&CK provides a global framework for mapping tactics and techniques.

11:00 Airflow acts as the orchestration backbone for ingestion graph transformation and LLM workflows.

13:00 Graph databases provide a full relationship view of attackers’ systems and entities.

14:00 LLMs automate mapping activity to MITRE ATT&CK and assign explainable risk scores.

17:00 Traditional signature-based detection allows lateral movement and exfiltration before teams can react.

18:00 End-to-end automation is essential to mitigating modern cybersecurity threats.

20:00 Future opportunities include deeper LLM integration as first-class citizens within Airflow.

Resources Mentioned:

Karan Alang

https://www.linkedin.com/in/karan-alang-4173437

Versa Networks | LinkedIn

https://www.linkedin.com/company/versa-networks

Versa Networks | Website

https://versa-networks.com

https://cloud.google.com/composer

Microsoft Defender XDR

https://www.microsoft.com/es-es/security/business/siem-and-xdr/microsoft-defender-xdr

Neo4j (Graph Database)

https://neo4j.com

MITRE ATT&CK Framework

https://attack.mitre.org

#AI #Automation #Airflow #MachineLearning

Using Plugins To Customize Airflow at Ponder Labs with Egor Tarasenko

support@contentallies.com (Astronomer) — Thu, 05 Mar 2026 00:15:00 -0500

In this episode, we explore how teams scale Apache Airflow in complex environments and what it takes to make orchestration work across many stakeholders. We look at real-world challenges around visibility, ownership and predictability as data platforms grow.

Egor Tarasenko, Data and AI Engineer at Ponder Labs, joins us to share how Ponder Labs customizes Airflow for education organizations using plugins, event-driven architectures and AI-powered tooling. He explains how his team supports large charter school networks and why structure, consistency and extensibility become critical at scale.

Key Takeaways:

00:00 Introduction.

01:21 Ponder Labs helps education organizations bring data from many systems together so it becomes useful for teachers, school leaders and administrators.

03:10 Airflow serves as the backbone for orchestrating ingestion, transformation and reverse ETL across client data platforms.

05:43 Everything is triggered from Airflow to maintain dependency, visibility and a single operational picture.

09:05 Managing hundreds of DAGs requires a focus on structure, visibility and consistency across teams.

09:51 Treating DAGs like APIs helps teams scale without needing deep knowledge of upstream logic.

12:00 Custom plugins like schedule insights help predict DAG run times across layered dependencies.

15:00 AI-powered Airflow chat enables non-technical stakeholders to understand DAG ownership dependencies and cluster activity.

22:06 Migrating plugins to Airflow 3 improves developer experience through cleaner APIs and faster extensibility.

Resources Mentioned:

Egor Tarasenko

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

https://airflow.apache.org

Astronomer Astro Platform

https://www.getdbt.com

https://www.astronomer.io

Egor Tarasenko on Substack

https://egortarasenko.substack.com

#AI #Automation #Airflow

Scaling Airflow at Wix for Analytics and AI with Ethan Shalev

support@contentallies.com (Astronomer) — Thu, 26 Feb 2026 00:00:00 -0500

Modern data orchestration at scale demands reliability, speed and thoughtful adoption of new tooling. As organizations grow, keeping pipelines efficient while supporting more teams becomes a critical challenge.

In this episode, we’re joined by Ethan Shalev, Data Engineer at Wix, to discuss how Wix operates Airflow at massive scale, migrates to Airflow 3 and uses AI to accelerate development.

Key Takeaways:

00:00 Introduction.

02:13 Wix structures data engineering across multiple product-focused organizations.

03:40 Migrating nearly 8,000 DAGs to Airflow 3 requires careful planning.

04:31 Migration creates an opportunity to remove long-standing legacy Airflow code.

05:32 Internal playbooks and Cursor rules standardize and speed up DAG migrations.

07:39 Airflow 3 introduces backfills, DAG versioning and asset-aware scheduling.

09:16 Deferrable operators reduce scheduler congestion in large Airflow environments.

12:54 AI-generated code still requires review and strong testing practices.

14:52 Moving to managed Airflow reduces operational burden on internal platform teams.

15:57 Improving multi-tenancy and UI personalization remains a key Airflow need.

Resources Mentioned:

Ethan Shalev

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

Wix | LinkedIn

https://www.linkedin.com/company/wix-com/

Wix | Website

https://www.wix.com/

https://airflow.apache.org/

https://www.astronomer.io/

Trino

https://trino.io/

Apache Iceberg

https://iceberg.apache.org/

Cursor

https://cursor.sh/

Carlos Daniel Puerto Niño

https://airflowsummit.org/

#AI #Automation #Airflow

Using Airflow To Orchestrate Billions of Events at Addi with Carlos Daniel Puerto Niño

support@contentallies.com (Astronomer) — Thu, 19 Feb 2026 00:10:00 -0500

Strong data orchestration is as much about culture and visibility as it is about technology. As data platforms scale, teams need systems that reduce cognitive load while increasing reliability and observability.

In this episode, Carlos Daniel Puerto Niño, Senior Analytics Engineer and Data Analyst at Addi, joins us to share how Addi uses Airflow to support batch orchestration, manage organizational complexity and improve monitoring across its data platform.

Key Takeaways:

00:00 Introduction.

01:25 Changes in company strategy increase data platform complexity over time.

04:00 Centralized data teams help manage organizational and technical change.

06:08 Scalable architectures support growing data volumes and use cases.

09:10 Adopting orchestration tools introduces operational and maintenance challenges.

14:43 Abstraction layers lower technical barriers for onboarding new team members.

15:36 Modularity and visibility improve the reliability of data pipelines.

18:14 Integrated monitoring supports faster incident response and resolution.

22:19 Limited access to orchestration metadata constrains proactive analysis.

Resources Mentioned:

https://www.linkedin.com/in/carlospuertoni%C3%B1o/

Addi | LinkedIn

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

Addi | Website

https://www.addi.com

https://airflow.apache.org/

https://www.astronomer.io/

Databricks

https://www.databricks.com/

https://www.getdbt.com/

https://grafana.com/

Slack

https://slack.com/

#AI #Automation #Airflow

Building Event-Driven Data Pipelines With Airflow 3 at Astrafy with Andrea Bombino

support@contentallies.com (Astronomer) — Thu, 12 Feb 2026 08:34:03 -0500

Real-time data expectations are reshaping how modern data teams think about orchestration and dependencies. As event-driven architectures become more common, teams need to rethink how pipelines react to data changes, rather than schedules.

In this episode, Andrea Bombino, Co-Founder and Head of Analytics Engineering at Astrafy, joins us to discuss how event-driven scheduling in Airflow is evolving and how Astrafy applies it to deliver faster, more responsive data pipelines.

Key Takeaways:

00:00 Introduction.

02:02 Astrafy’s role in guiding clients across the modern data stack.

03:15 Strong DAG dependencies create challenges for time-based scheduling.

04:48 Event-driven pipelines respond to increasing real-time data demands.

05:30 Airflow 3 introduces native support for event-driven orchestration.

06:27 Sensor-based workflows reveal scalability and efficiency limitations.

11:32 Event-driven assets improve efficiency and pipeline elegance.

14:45 Governance and cross-instance coordination emerge as ongoing challenges.

Resources Mentioned:

Andrea Bombino

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

Astrafy | LinkedIn

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

Astrafy | Website

https://www.astrafy.io

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

#AI #Automation #Airflow

Uphold’s Approach to Orchestrating Modern Data Workflows with Jaime Oliveira

support@contentallies.com (Astronomer) — Thu, 05 Feb 2026 00:10:00 -0500

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

https://www.getdbt.com

https://www.snowflake.com

https://kubernetes.io

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

support@contentallies.com (Astronomer) — Thu, 29 Jan 2026 00:05:00 -0500

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

support@contentallies.com (Astronomer) — Thu, 22 Jan 2026 07:05:57 -0500

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

support@contentallies.com (Astronomer) — Thu, 15 Jan 2026 00:00:00 -0500

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/

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

support@contentallies.com (Astronomer) — Thu, 11 Dec 2025 04:02:06 -0500

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/

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

support@contentallies.com (Astronomer) — Thu, 04 Dec 2025 03:33:35 -0500

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

support@contentallies.com (Astronomer) — Thu, 20 Nov 2025 00:50:13 -0500

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

support@contentallies.com (Astronomer) — Thu, 13 Nov 2025 06:24:37 -0500

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/

Google Cloud Composer (Managed Airflow)

https://www.astronomer.io/

AWS Managed Airflow

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

https://cloud.google.com/composer

https://airflowsummit.org/

#AI #Automation #Airflow #MachineLearning

How Redica Transformed Their Data With Airflow and Snowflake with Shankar Mahindar

support@contentallies.com (Astronomer) — Thu, 06 Nov 2025 04:23:27 -0500

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

https://airflow.apache.org/

https://www.astronomer.io/

https://www.snowflake.com/

AWS

https://aws.amazon.com/

#AI #Automation #Airflow #MachineLearning

How Airflow and AI Power Investigative Journalism at the Financial Times with Zdravko Hvarlingov

support@contentallies.com (Astronomer) — Thu, 30 Oct 2025 05:48:07 -0400

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/

UK Register of Members’ Financial Interests

https://airflow.apache.org/

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/

Airflow Kubernetes Executor

https://kubernetes.io/

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

GitHub

https://github.com/

#AI #Automation #Airflow #MachineLearning

Inside Vinted’s Code-Generated Airflow Pipelines with Oscar Ligthart and Rodrigo Loredo

support@contentallies.com (Astronomer) — Thu, 23 Oct 2025 01:25:27 -0400

The shift from monolithic to decentralized data workflows changes how teams build, connect and scale pipelines.

In this episode, we feature Oscar Ligthart, Lead Data Engineer, and Rodrigo Loredo, Lead Analytics Engineer, both at Vinted, as we unpack their YAML-driven abstraction that generates Airflow DAGs and standardizes cross-team orchestration.

Key Takeaways:

00:00 Introduction.

05:28 Challenges of decentralization.

06:45 YAML-based generator standardizes pipelines and dependencies.

12:28 Declarative assets and sensors align cross-DAG dependencies.

17:29 Task-level callbacks enable auto-recovery and clear ownership.

21:39 Standardized building blocks simplify upgrades and maintenance.

24:52 Platform focus frees domain work.

26:49 Container-only standardization prevents sprawl.

Resources Mentioned:

Oscar Ligthart

https://www.linkedin.com/in/oscar-ligthart/

Rodrigo Loredo

https://www.linkedin.com/in/rodrigo-loredo-410a16134/

Vinted | LinkedIn

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

Vinted | Website

https://www.vinted.com/?srsltid=AfmBOor87MGR_eLOauCO93V9A-aLDaAhGYx9cnu_oN8s1SAXMlCRuhW7

https://airflow.apache.org/

https://kubernetes.io/

Airflow Datasets & Assets (concepts)

https://www.getdbt.com/

Google Cloud Vertex AI

https://cloud.google.com/vertex-ai

https://www.astronomer.io/docs/learn/airflow-datasets

https://airflowsummit.org/

#AI #Automation #Airflow #MachineLearning

Transforming Data Pipelines at XENA Intelligence with Naseem Shah

support@contentallies.com (Astronomer) — Thu, 16 Oct 2025 05:36:36 -0400

The shift from simple cron jobs to orchestrated AI-powered workflows is reshaping how startups scale. For a small team, these transitions come with unique challenges and big opportunities.

In this episode, Naseem Shah, Head of Engineering at Xena Intelligence, shares how he built data pipelines from scratch, adopted Apache Airflow and transformed Amazon review analysis with LLMs.

Key Takeaways:

00:00 Introduction.

03:28 The importance of building initial products that support growth and investment.

06:16 The process of adopting new tools to improve reliability and efficiency.

09:29 Approaches to learning complex technologies through practice and fundamentals.

13:57 Trade-offs small teams face when balancing performance and costs.

18:40 Using AI-driven approaches to generate insights from large datasets.

22:38 How unstructured data can be transformed into actionable information.

25:55 Moving from manual tasks to fully automated workflows.

28:05 Orchestration as a foundation for scaling advanced use cases.

Resources Mentioned:

Naseem Shah

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

Xena Intelligence | LinkedIn

https://www.linkedin.com/company/xena-intelligence/

Xena Intelligence | Website

https://xenaintelligence.com/

https://airflow.apache.org/

Google Cloud Composer

https://cloud.google.com/composer

Techstars

https://www.techstars.com/

Docker

https://www.docker.com/

AWS SQS

https://aws.amazon.com/sqs/

PostgreSQL

https://www.postgresql.org/

#AI #Automation #Airflow #MachineLearning

Scaling Geospatial Workflows With Airflow at Overture Maps Foundation and Wherobots with Alex Iannicelli and Daniel Smith

support@contentallies.com (Astronomer) — Thu, 09 Oct 2025 00:00:00 -0400

Using Airflow to orchestrate geospatial data pipelines unlocks powerful efficiencies for data teams. The combination of scalable processing and visual observability streamlines workflows, reduces costs and improves iteration speed.

In this episode, Alex Iannicelli, Staff Software Engineer at Overture Maps Foundation, and Daniel Smith, Senior Solutions Architect at Wherobots, join us to discuss leveraging Apache Airflow and Apache Sedona to process massive geospatial datasets, build reproducible pipelines and orchestrate complex workflows across platforms.

Key Takeaways:

00:00 Introduction.

03:22 How merging multiple data sources supports comprehensive datasets.

04:20 The value of flexible configurations for running pipelines on different platforms.

06:35 Why orchestration tools are essential for handling continuous data streams.

09:45 The importance of observability for monitoring progress and troubleshooting issues.

11:30 Strategies for processing large, complex datasets efficiently.

13:27 Expanding orchestration beyond core pipelines to automate frequent tasks.

17:02 Advantages of using open-source operators to simplify integration and deployment.

20:32 Desired improvements in orchestration tools for usability and workflow management.

Resources Mentioned:

Alex Iannicelli

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

Overture Maps Foundation | LinkedIn

https://www.linkedin.com/company/overture-maps-foundation/

Overture Maps Foundation | Website

https://overturemaps.org

Daniel Smith

https://www.linkedin.com/in/daniel-smith-analyst/

Wherobots | LinkedIn

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

Wherobots | Website

https://www.wherobots.com

https://airflow.apache.org/

Apache Sedona

https://sedona.apache.org/

Github repo

https://github.com/wherobots/airflow-providers-wherobots

#AI #Automation #Airflow #MachineLearning

Scaling Airflow for Enterprise Data Platforms at PepsiCo with Kunal Bhattacharya

support@contentallies.com (Astronomer) — Thu, 02 Oct 2025 00:00:00 -0400

PepsiCo’s data platform drives insights across finance, marketing and data science. Delivering stability, scalability and developer delight is central to its success, and engineering leadership plays a key role in making this possible.

In this episode, Kunal Bhattacharya, Senior Manager of Data Platform Engineering at PepsiCo, shares how his team manages Airflow at scale while ensuring security, performance and cost efficiency.

Key Takeaways:

00:00 Introduction.

02:31 Enabling developer delight by extending platform capabilities.

03:56 Role of Snowflake, dbt and Airflow in PepsiCo’s data stack.

06:10 Local developer environments built using official Airflow Helm charts.

07:13 Pre-staging and PR environments as testing playgrounds.

08:08 Automating labeling and resource allocation via DAG factories.

12:16 Cost optimization through pod labeling and Datadog insights.

14:01 Isolating dbt engines to improve performance across teams.

16:12 Wishlist for Airflow 3: Improved role-based grants and database modeling.

Resources Mentioned:

Kunal Bhattacharya

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

PepsiCo | LinkedIn

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

PepsiCo | Website

https://www.pepsico.com

https://airflow.apache.org/

https://www.snowflake.com

https://www.getdbt.com

https://kubernetes.io

Great Expectations

https://greatexpectations.io

Monte Carlo

https://www.montecarlodata.com

#AI #Automation #Airflow #MachineLearning

Building a Unified Data Platform at Pattern with William Graham

support@contentallies.com (Astronomer) — Thu, 25 Sep 2025 00:00:00 -0400

The orchestration of data workflows at scale requires both flexibility and security. At Pattern, decoupling scheduling from orchestration has reshaped how data teams manage large-scale pipelines.

In this episode, we are joined by William Graham, Senior Data Engineer at Pattern, who explains how his team leverages Apache Airflow alongside their open-source tool Heimdall to streamline scheduling, orchestration and access management.

Key Takeaways:

00:00 Introduction.

02:44 Structure of Pattern’s data teams across acquisition, engineering and platform.

04:27 How Airflow became the central scheduler for batch jobs.

08:57 Credential management challenges that led to decoupling scheduling and orchestration.

12:21 Heimdall simplifies multi-application access through a unified interface.

13:15 Standardized operators in Airflow using Heimdall integration.

17:13 Open-source contributions and early adoption of Heimdall within Pattern.

21:01 Community support for Airflow and satisfaction with scheduling flexibility.

Resources Mentioned:

William Graham

https://www.linkedin.com/in/willgraham2/

Pattern | LinkedIn

https://www.linkedin.com/company/pattern-hq/

Pattern | Website

https://pattern.com

https://airflow.apache.org

Heimdall on GitHub

https://github.com/patterninc/heimdall

Netflix Genie

https://netflix.github.io/genie/

#AI #Automation #Airflow #MachineLearning

How Astronomer Turns Proactive Monitoring Into Customer Success with Collin McNulty

support@contentallies.com (Astronomer) — Thu, 18 Sep 2025 00:00:00 -0400

The evolution of Airflow continues to shape data orchestration and monitoring strategies. Leveraging it beyond traditional ETL use cases opens powerful new possibilities for proactive support and internal operations.

In this episode, we are joined by Collin McNulty, Sr. Director of Global Support at Astronomer, who shares insights from his journey into data engineering and the lessons learned from leading Astronomer’s Customer Reliability Engineering (CRE) team.

Key Takeaways:

00:00 Introduction.

03:07 Lessons learned in adapting to major platform transitions.

05:18 How proactive monitoring improves reliability and customer experience.

08:10 Using automation to enhance internal support processes.

12:09 Why keeping systems current helps avoid unnecessary issues.

15:14 Approaches that strengthen system reliability and efficiency.

18:46 Best practices for simplifying complex orchestration dependencies.

23:24 Anticipated innovations that expand orchestration capabilities.

Resources Mentioned:

Collin McNulty

https://www.linkedin.com/in/collin-mcnulty/

Astronomer | LinkedIn

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

Astronomer | Website

https://www.astronomer.io

https://airflow.apache.org/

Prometheus

https://prometheus.io/

Splunk

https://www.splunk.com/

#AI #Automation #Airflow #MachineLearning

Overcoming Data Engineering Challenges at Daiichi Sankyo Europe GmbH with Evgenii Prusov

support@contentallies.com (Astronomer) — Thu, 11 Sep 2025 06:58:49 -0400

The shift to a unified data platform is reshaping how pharmaceutical companies manage and orchestrate data. Establishing standards across regions and teams ensures scalability and efficiency in handling large-scale analytics.

In this episode, Evgenii Prusov, Senior Data Platform Engineer of Daiichi Sankyo Europe GmbH, joins us to discuss building and scaling a centralized data platform with Airflow and Astronomer.

Key Takeaways:

00:00 Introduction.

02:49 Building a centralized data platform for 15 European countries.

05:19 Adopting SaaS to manage Airflow from day one.

07:01 Leveraging Airflow for data orchestration across products.

08:16 Teaching non-Python users how to work with Airflow is challenging.

12:25 Creating a global data community across Europe, the US and Japan.

14:04 Monthly calls help share knowledge and align regional teams.

15:47 Contributing to the open-source Airflow project as a way to deepen expertise.

16:32 Desire for more guidelines, debugging tutorials and testing best practices in Airflow.

Resources Mentioned:

Evgenii Prusov

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

Daiichi Sankyo Europe GmbH | LinkedIn

https://www.linkedin.com/company/daiichi-sankyo-europe-gmbh/

Daiichi Sankyo Europe GmbH | Website

https://www.daiichi-sankyo.eu

https://airflow.apache.org/

https://www.astronomer.io/

https://www.snowflake.com/

#AI #Automation #Airflow #MachineLearning

Building a Data-Driven Beauty and Wellness Marketplace at StyleSeat with Paschal Onuorah

support@contentallies.com (Astronomer) — Thu, 04 Sep 2025 05:54:20 -0400

StyleSeat is revolutionizing how beauty and wellness professionals grow their businesses through data-driven tools. From streamlining scheduling to optimizing marketing, their platform empowers professionals to focus on their craft while expanding their client base.

In this episode, Paschal Onuorah, Senior Data Engineer at StyleSeat, shares how the company leverages Airflow, dbt, and Cosmos to drive marketplace intelligence, improve client connections and deliver measurable growth for professionals.

Key Takeaways:

00:00 Introduction.

05:44 The role of the data engineering team in driving business success.

08:52 Leveraging technology for real-time business intelligence.

10:52 Data-driven strategies for improving marketing outcomes.

13:05 How adopting the right tools can increase revenue growth.

14:25 Advantages of simplifying and integrating technical workflows.

18:45 Benefits of multi-environment configurations for development and production.

20:17 Foundational skills and best practices for learning Airflow effectively.

22:33 Opportunities for deeper tool integration and improved data visualization.

Resources Mentioned:

Paschal Onuorah

https://www.linkedin.com/in/onuorah-paschal/

StyleSeat | LinkedIn

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

StyleSeat | Website

https://www.styleseat.com

https://airflow.apache.org/

https://www.getdbt.com/

https://www.astronomer.io/cosmos/

#AI #Automation #Airflow #MachineLearning

Building the Future of Airflow Execution at Astronomer with Ian Buss and Piotr Chomiak

support@contentallies.com (Astronomer) — Thu, 28 Aug 2025 00:00:00 -0400

The evolution of orchestration in Airflow continues with innovations that address both scalability and security. From improving executor reliability to enabling remote execution, these advancements reshape how organizations manage data pipelines.

In this episode, we’re joined by Ian Buss, Principal Software Engineer at Astronomer, and Piotr Chomiak, Principal Product Manager at Astronomer, who share insights into the Astro Executor and remote execution.

Key Takeaways:

00:00 Introduction.

04:13 How product leadership drives scalability for enterprise needs.

08:23 Architectural changes that improve reliability and remove bottlenecks.

10:15 Metrics that enhance visibility into system performance.

12:54 The role of remote execution in addressing security requirements.

15:56 Differences between open-source solutions and managed offerings.

19:04 Broad industry adoption and applicability of remote execution.

20:39 Future advancements in language support and multi-tenancy.

Resources Mentioned:

Ian Buss

https://www.linkedin.com/in/ian-buss/

Piotr Chomiak

https://www.linkedin.com/in/piotr-chomiak-b1955624/

Astronomer | Website

https://www.astronomer.io

Beyond Analytics conference

https://airflow.apache.org/

Airflow Slack Community

https://airflow.apache.org/community/

https://astronomer.io/beyond/dataflowcast

#AI #Automation #Airflow #MachineLearning

Scaling On-Prem Airflow With 2,000 DAGs at Numberly with Sébastien Crocquevieille

support@contentallies.com (Astronomer) — Thu, 21 Aug 2025 00:00:00 -0400

Scaling 2,000+ data pipelines isn’t easy. But with the right tools and a self-hosted mindset, it becomes achievable.

In this episode, Sébastien Crocquevieille, Data Engineer at Numberly, unpacks how the team scaled their on-prem Airflow setup using open-source tooling and Kubernetes. We explore orchestration strategies, UI-driven stakeholder access and Airflow’s evolving features.

Key Takeaways:

00:00 Introduction.

02:13 Overview of the company’s operations and global presence.

04:00 The tech stack and structure of the data engineering team.

04:24 Running nearly 2,000 DAGs in production using Airflow.

05:42 How Airflow’s UI empowers stakeholders to self-serve and troubleshoot.

07:05 Details on the Kubernetes-based Airflow setup using Helm charts.

09:31 Transition from GitSync to NFS for DAG syncing due to performance issues.

14:11 Making every team member Airflow-literate through local installation.

17:56 Using custom libraries and plugins to extend Airflow functionality.

Resources Mentioned:

Sébastien Crocquevieille

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

Numberly | LinkedIn

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

Numberly | Website

https://numberly.com/

https://airflow.apache.org/

Helm Chart for Apache Airflow

https://grafana.com/

Apache Kafka

https://kafka.apache.org/

https://airflow.apache.org/docs/helm-chart/stable/index.html

KubernetesPodOperator – Airflow

https://kubernetes.io/

GitLab

https://about.gitlab.com/

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

Beyond Analytics Conference

https://astronomer.io/beyond/dataflowcast

#AI #Automation #Airflow #MachineLearning

How Moniepoint Group Uses Airflow for Exposure Monitoring with Adeolu Adegboye

support@contentallies.com (Astronomer) — Thu, 14 Aug 2025 04:50:00 -0400

Managing financial data at scale requires precise orchestration and proactive monitoring to maintain operational efficiency.

In this episode, we are joined by Adeolu Adegboye, Data Engineer at Moniepoint Group, who shares how his team uses data pipelines and workflow automation to manage high volumes of transactions, ensure timely alerts and support diverse stakeholders across the business.

Key Takeaways:

(00:00) Introduction.

(02:48) The role of data engineering in supporting all business operations.

(04:17) Leveraging workflow orchestration to manage daily processes.

(05:20) Proactively monitoring for anomalies to prevent potential issues.

(08:12) Simplifying complex insights for non-technical teams.

(13:01) Improving efficiency through dynamic and parallel workflows.

(14:19) Optimizing system performance to handle large-scale operations.

(17:19) Exploring creative and innovative uses for workflow automation.

Resources Mentioned:

Adeolu Adegboye

https://www.linkedin.com/in/adeolu-adegboye/

Moniepoint Group | LinkedIn

https://www.linkedin.com/company/moniepoint-inc/

Moniepoint Group | Website

https://www.moniepoint.com

https://airflow.apache.org/

ClickHouse

https://clickhouse.com/

Beyond Analytics Conference

https://grafana.com/

https://astronomer.io/beyond/dataflowcast

#AI #Automation #Airflow #MachineLearning

Inside Bosch’s Airflow 3 Revolution: Remote Execution with Jens Scheffler

support@contentallies.com (Astronomer) — Thu, 07 Aug 2025 00:00:00 -0400

The evolution of Airflow has reached a milestone with the introduction of remote execution in Airflow 3, enabling flexible orchestration across distributed environments.

In this episode, Jens Scheffler, Test Execution Cluster Technical Architect at Bosch, shares insights on how his team’s need for large-scale, cross-environment testing influenced the development of the Edge Executor and shaped this major release.

Key Takeaways:

(02:39) The role of remote execution in supporting large-scale testing needs.

(04:44) How community support contributed to the Edge Executor’s development.

(08:41) Navigating network and infrastructure limitations within secure environments.

(13:25) Transitioning from database-heavy processes to an API-driven model.

(14:16) How the new task SDK in Airflow 3 improves distributed task execution.

(16:54) What is required to set up and configure the Edge Executor.

(19:36) Managing multiple queues to optimize tasks across different environments.

(23:30) Examples of extreme distance use cases for edge execution.

Resources Mentioned:

Jens Scheffler

https://www.linkedin.com/in/jens-scheffler/

Bosch | LinkedIn

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

Bosch | Website

https://www.bosch.com/

Edge Executor (Edge3 Provider Package)

https://airflow.apache.org/

https://airflow.apache.org/docs/apache-airflow/stable/core-concepts/executor/index.html

Astronomer’s Astro Executor

https://www.astronomer.io/docs/astro/astro-executor/

Beyond Analytics Conference

https://astronomer.io/beyond/dataflowcast

#AI #Automation #Airflow #MachineLearning

Inside Modern Data Infrastructure at Massdriver with Cory O’Daniel and Jake Ferriero

support@contentallies.com (Astronomer) — Thu, 31 Jul 2025 04:50:00 -0400

Managing modern data platforms means navigating a web of complex infrastructure, competing team needs and evolving security standards. For data teams to truly thrive, infrastructure must become both accessible and compliant without sacrificing velocity or reliability.

In this episode, we’re joined by Cory O’Daniel, CEO and Co-Founder at Massdriver, and Jacob Ferriero, Senior Software Engineer at Astronomer, to unpack what it takes to make data platform engineering scalable, sustainable and secure. They share lessons from years of experience working with DevOps, ML teams and platform engineers and discuss how Airflow fits into the orchestration layer of today’s data stacks.

Key Takeaways:

(03:27) Making infrastructure accessible without deep ops knowledge.

(07:23) Distinct personas and responsibilities across data teams.

(09:53) Infrastructure hurdles specific to ML workloads.

(11:13) Compliance and governance shaping platform design.

(13:27) Tooling mismatches between teams cause friction.

(15:13) Airflow’s orchestration role within broader system architecture.

(22:10) Creating reusable infrastructure patterns for consistency.

(24:13) Enabling secure access without slowing down development.

(26:55) Opportunities to improve Airflow with event-driven and reliability tooling.

Resources Mentioned:

Cory O’Daniel

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

Massdriver | LinkedIn

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

Massdriver | Website

https://www.massdriver.cloud/

Jacob Ferriero

https://www.linkedin.com/in/jacob-ferriero/

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

https://airflow.apache.org/

Prequel

https://www.prequel.co/

#AI #Automation #Airflow #MachineLearning

The Future of Airflow Telemetry with Bolke de Bruin

support@contentallies.com (Astronomer) — Thu, 17 Jul 2025 07:40:00 -0400

Telemetry has the potential to guide the future of Airflow, but only if it’s implemented transparently and with community trust.

In this episode, we’re joined by Bolke de Bruin, Director at Metyis and a long-time Airflow PMC member. Bolke discusses how telemetry has been handled in the past, why it matters now and what it will take to get it right.

Key Takeaways:

(03:20) The role of foundations in establishing credibility and sustainability.

(04:52) Why data collection is critical to open-source project direction.

(07:24) Lessons learned from previous approaches to user data collection.

(10:23) The current state of telemetry in the project.

(10:53) Community trust as a prerequisite for technical implementation.

(12:54) The importance of managing sensitive data within trusted ecosystems.

(16:37) Ethical considerations in balancing participation and access.

(18:45) Forward-looking ideas for improving workflow design and usability.

Resources Mentioned:

Bolke de Bruin

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

Metyis | LinkedIn

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

Metyis | Website

http://www.metyis.com

https://airflow.apache.org/

https://airflowsummit.org/

Airflow Dev List

https://lists.apache.org/list.html?dev@airflow.apache.org

https://www.astronomer.io/events/roadshow/new-york/

https://www.astronomer.io/events/roadshow/san-francisco/

#AI #Automation #Airflow #MachineLearning

Transforming the Airflow UI for Cloudera’s Users with Shubham Raj

support@contentallies.com (Astronomer) — Thu, 10 Jul 2025 00:00:00 -0400

Contributing to open-source projects can be daunting, but it can also unlock unexpected innovation. This episode showcases how one engineer’s journey with Apache Airflow led to impactful UI enhancements and infrastructure solutions at scale. Shubham Raj, Software Engineer II at Cloudera, shares how his team built a drag-and-drop DAG editor for non-coders, contributions which helped shape the Airflow 3.0 Ul and introduced features like external XCom control and bulk APls.

Key Takeaways:

(02:30) Day-to-day responsibilities building platforms that simplify orchestration.

(05:27) Factors that make onboarding into large open-source projects accessible.

(07:35) The value of improved user interfaces for task state visibility and control.

(09:49) Enabling faster debugging by exposing internal data through APIs.

(13:00) Balancing frontend design goals with backend functionality.

(14:19) Creating workflow editors that lower the barrier to entry.

(16:54) Supporting a variety of task types within a visual DAG builder.

(19:32) Common infrastructure challenges faced by orchestration users.

(20:37) Addressing dependency management across distributed environments.

Resources Mentioned:

Shubham Raj

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

Cloudera | LinkedIn

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

Cloudera | Website

https://www.cloudera.com/

https://airflow.apache.org/

2023 Airflow Summit

https://airflowsummit.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

Streamlining Thousands of Data Pipelines at Lyft with Yunhao Qing

support@contentallies.com (Astronomer) — Mon, 07 Jul 2025 00:00:00 -0400

Managing data pipelines at scale is not just a technical challenge. It is also an organizational one. At Lyft, success means empowering dozens of teams to build with autonomy while enforcing governance and best practices across thousands of workflows.

In this episode, we speak with Yunhao Qing, Software Engineer at Lyft, about building a governed data-engineering platform powered by Airflow that balances flexibility, standardization and scale.

Key Takeaways:

(03:17) Supporting internal teams with a centralized orchestration platform.

(04:54) Migrating to a managed service to reduce infrastructure overhead.

(06:04) Embedding platform-level governance into custom components.

(08:02) Consolidating and regulating the creation of custom code.

(09:48) Identifying and correcting inefficient workflow patterns.

(11:17) Replacing manual workarounds with native platform features.

(14:32) Preparing teams for major version upgrades.

(16:03) Leveraging asset-based scheduling for smarter triggers.

(18:13) Envisioning GenAI and semantic search for future productivity.

Resources Mentioned:

Yunhao Qing

https://www.linkedin.com/in/yunhao-qing

Lyft | LinkedIn

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

Lyft | Website

https://www.lyft.com/

https://airflow.apache.org/

https://www.astronomer.io/

Airflow Fundamentals Certification

https://kubernetes.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

Transforming Customer Education in Data Engineering at Astronomer with Marc Lamberti

support@contentallies.com (Astronomer) — Thu, 26 Jun 2025 03:44:47 -0400

Understanding the complexities of Apache Airflow can be daunting for newcomers and seasoned data engineers. But with the right guidance, mastering the tool becomes an achievable milestone.

In this episode, Marc Lamberti, Head of Customer Education at Astronomer, joins us to share his journey from Udemy instructor to driving education at Astronomer, and how he's helping over 100,000 learners demystify Airflow.

Key Takeaways:

(02:36) Early exposure to Airflow while addressing inefficiencies in data workflows.

(04:10) Common barriers to implementing open source tools in enterprise settings.

(06:18) The shift from part-time teaching to a full-time focus on Airflow education.

(07:53) A modular, guided approach to structuring educational content.

(09:57) The value of highlighting underused Airflow features for broader adoption.

(12:35) Certifications as a method to assess readiness and uncover knowledge gaps.

(13:25) Coverage of essential Airflow concepts in the Fundamentals exam.

(16:07) The DAG Authoring exam’s emphasis on practical, advanced features.

(20:08) A call for more visible integration of Airflow with AI workflows.

Resources Mentioned:

Marc Lamberti

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

Astronomer | LinkedIn

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

Astronomer Academy

https://academy.astronomer.io/

https://www.astronomer.io/certification/

DAG Authoring Certification

https://academy.astronomer.io/plan/astronomer-certification-dag-authoring-for-apache-airflow-exam

The Complete Hands-On Introduction to Airflow

https://www.udemy.com/course/the-complete-hands-on-course-to-master-apache-airflow/?utm_source=adwords&utm_medium=udemyads&utm_campaign=Search_DSA_Beta_Prof_la.EN_cc.ROW-English&campaigntype=Search&portfolio=ROW-English&language=EN&product=Course&test=&audience=DSA&topic=&priority=Beta&utm_content=deal4584&utm_term=_._ag_162511579404_._ad_696197165418_._kw__._de_c_._dm__._pl__._ti_dsa-1677053911088_._li_9061346_._pd__._&matchtype=&gad_source=1&gad_campaignid=21168154305&gbraid=0AAAAADROdO3MpljfP-gssiYSmDEPdhZV9&gclid=Cj0KCQjw097CBhDIARIsAJ3-nxdjZA6G5-Y0-akk6Huksy2PLb04t92J4iNfUSIbMdrSAla_tb-o2N8aArOeEALw_wcB&couponCode=PMNVD3025

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

Embracing Data Mesh and SQL Sensors for Scalable Workflows at lastminute.com with Alberto Crespi

support@contentallies.com (Astronomer) — Fri, 20 Jun 2025 02:18:19 -0400

The flexibility of Airflow plays a pivotal role in enabling decentralized data architectures and empowering cross-functional teams.

In this episode, we speak with Alberto Crespi, Data Architect at lastminute.com, who shares how his team scales Airflow across 12 teams while supporting both vertical and horizontal structures under a data mesh approach.

Key Takeaways:

(02:17) Defining responsibilities within data architecture teams.

(04:15) Consolidating multiple orchestrators into a single solution.

(07:00) Scaling Airflow environments with shared infrastructure and DevOps practices.

(10:59) Managing dependencies and readiness using SQL sensors.

(14:23) Enhancing visibility and response through Slack-integrated monitoring.

(19:28) Extending Airflow’s flexibility to run legacy systems.

(22:28) Integrating transformation tools into orchestrated pipelines.

(25:54) Enabling non-engineers to contribute to pipeline development.

(27:33) Fostering adoption through collaboration and communication.

Resources Mentioned:

Alberto Crespi

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

lastminute.com | Website

https://lastminute.com

https://airflow.apache.org/

https://www.getdbt.com/

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

GitLab Slack

https://slack.com/

https://kubernetes.io/

Confluence

https://www.atlassian.com/software/confluence

Slack

https://slack.com/

https://www.astronomer.io/events/roadshow/new-york/

https://www.astronomer.io/events/roadshow/san-francisco/

#AI #Automation #Airflow #MachineLearning

The AI-Ready Pipeline: Reimagining Airflow at Veyer® Logistics with Anu Pabla

support@contentallies.com (Astronomer) — Thu, 12 Jun 2025 06:48:03 -0400

Innovation in orchestration is redefining how engineers approach both traditional ETL pipelines and emerging AI workloads. Understanding how to harness Airflow’s flexibility and observability is essential for teams navigating today’s evolving data landscape.

In this episode, Anu Pabla, Principal Engineer at The ODP Corporation, joins us to discuss her journey from legacy orchestration patterns to AI-native pipelines and why she sees Airflow as the future of AI workload orchestration.

Key Takeaways:

(03:43) Engaging with external technology communities fosters innovation.

(05:05) Mentoring early-career engineers builds confidence in a complex tech landscape.

(07:51) Orchestration patterns continue to evolve with modern data needs.

(08:41) Managing AI workflows requires structured and flexible orchestration.

(10:35) High-quality, meaningful data remains foundational across use cases.

(15:08) Community-driven open source tools offer lasting value.

(16:59) Self-healing systems support both legacy and AI pipelines.

(20:20) Orchestration platforms can drive future AI-native workloads.

Resources Mentioned:

Anu Pabla

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

The ODP Corporation

https://www.linkedin.com/company/the-odp-corporation/

The ODP Corporation | Website

https://www.theodpcorp.com/homepage

https://airflow.apache.org/

LlamaIndex

https://www.llamaindex.ai/

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

Streamlining AI and ML Operations at IBM with BJ Adesoji and Ryan Yackel

support@contentallies.com (Astronomer) — Thu, 05 Jun 2025 00:00:00 -0400

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

https://airflow.apache.org/

watsonx Orchestrate

https://www.ibm.com/products/watsonx-orchestrate

Domino

https://domino.ai/

https://www.astronomer.io/

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

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/new-york/

https://www.astronomer.io/events/roadshow/san-francisco/

#AI #Automation #Airflow #MachineLearning

Inside the Custom Framework for Managing Airflow Code at Wix with Gil Reich

support@contentallies.com (Astronomer) — Thu, 29 May 2025 00:00:00 -0400

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

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

support@contentallies.com (Astronomer) — Thu, 22 May 2025 00:00:00 -0400

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

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

support@contentallies.com (Astronomer) — Thu, 15 May 2025 00:00:00 -0400

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/

https://airflow.apache.org/

https://www.getdbt.com/

Great Expectations

https://greatexpectations.io/

https://www.astronomer.io/events/roadshow/new-york/

https://www.astronomer.io/events/roadshow/san-francisco/

#AI #Automation #Airflow #MachineLearning

Why Developer Experience Shapes Data Pipeline Standards at Next Insurance with Snir Israeli

support@contentallies.com (Astronomer) — Thu, 08 May 2025 01:50:06 -0400

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/

https://airflow.apache.org/

https://www.astronomer.io/events/roadshow/new-york/

https://www.astronomer.io/events/roadshow/san-francisco/

#AI #Automation #Airflow #MachineLearning

Data Quality and Observability at Tekmetric with Ipsa Trivedi

support@contentallies.com (Astronomer) — Thu, 01 May 2025 00:00:00 -0400

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/

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/new-york/

https://www.astronomer.io/events/roadshow/san-francisco/

#AI #Automation #Airflow #MachineLearning

Introducing Apache Airflow® 3 with Vikram Koka and Jed Cunningham

support@contentallies.com (Astronomer) — Thu, 24 Apr 2025 00:00:00 -0400

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

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/new-york/

https://www.astronomer.io/events/roadshow/san-francisco/

#AI #Automation #Airflow #MachineLearning

Airflow in Action: Powering Instacart's Complex Ecosystem

support@contentallies.com (Astronomer) — Thu, 17 Apr 2025 00:00:00 -0400

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

https://airflow.apache.org/

AWS Amazon

https://aws.amazon.com/ecs/

Terraform

https://www.terraform.io/

https://www.astronomer.io/events/roadshow/new-york/ https://www.astronomer.io/events/roadshow/sydney/

https://www.astronomer.io/events/roadshow/san-francisco/

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

support@contentallies.com (Astronomer) — Thu, 10 Apr 2025 00:00:00 -0400

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/

https://airflow.apache.org/

Astronomer Presents: Exploring Apache Airflow® 3 Roadshows

https://grafana.com/solutions/apache-airflow/monitor/

https://www.astronomer.io/events/roadshow/

https://www.astronomer.io/events/roadshow/new-york/

https://www.astronomer.io/events/roadshow/san-francisco/

#AI #Automation #Airflow #MachineLearning

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

support@contentallies.com (Astronomer) — Thu, 03 Apr 2025 01:24:00 -0400

The 2025 State of Airflow report sheds light on how global users are adopting, evolving and innovating with Apache Airflow. With over 5,000 responses from 116 countries, the survey reveals critical insights into Airflows’ role in business operations, new use cases and what’s ahead for the community.

In this episode, Tamara Fingerlin, Developer Advocate at Astronomer, walks us through her process of analyzing survey data, key trends from the report and what to expect from Airflow 3.0.

Key Takeaways:

(02:14) The State of Airflow report combines anonymized telemetry and survey results.

(03:25) The survey received thousands of responses from many countries, showcasing global reach.

(04:49) The survey process involves multiple steps, from question selection to report creation.

(09:00) Many users expect to increase Airflow usage for revenue-generating or external use cases.

(11:04) Experienced users tend to utilize Airflow more for advanced use cases like MLOps.

(15:13) UI improvements offer enhanced navigation and error visibility.

(18:15) Architectural changes enable new capabilities like remote execution and language support.

(19:40) Long-requested features will be available in the new major release.

(21:00) Future aspirations include integrating data visualization capabilities into the UI.

Resources Mentioned:

Tamara Fingerlin

https://www.linkedin.com/in/tamara-janina-fingerlin/

Astronomer | LinkedIn

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

Astronomer | Website

https://www.astronomer.io

2025 State of Airflow Webinar

https://airflow.apache.org/

https://www.astronomer.io/airflow/state-of-airflow/

Airflow Slack

https://apache-airflow-slack.herokuapp.com/

Astronomer Presents: Exploring Apache Airflow® 3 Roadshows

https://www.astronomer.io/events/roadshow/

https://www.astronomer.io/events/roadshow/new-york/

https://www.astronomer.io/events/roadshow/san-francisco/

#AI #Automation #Airflow #MachineLearning

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

support@contentallies.com (Astronomer) — Thu, 20 Mar 2025 00:37:00 -0400

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/

Astronomer Roadshow: Exploring Apache Airflow 3 | London

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/

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/

#AI #Automation #Airflow #MachineLearning

Building Scalable ML Infrastructure at Outerbounds with Savin Goyal

support@contentallies.com (Astronomer) — Thu, 13 Mar 2025 00:37:34 -0400

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/

Netflix’s Maestro Orchestration System -

https://airflow.apache.org/

Metaflow -

https://metaflow.org/

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/

#AI #Automation #Airflow #MachineLearning

Customizing Airflow for Complex Data Environments at Stripe with Nick Bilozerov and Sharadh Krishnamurthy

support@contentallies.com (Astronomer) — Thu, 06 Mar 2025 02:20:37 -0500

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/

https://airflow.apache.org/

Stripe | LinkedIn -

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

Stripe | Website -

https://stripe.com/

#AI #Automation #Airflow #MachineLearning

Harnessing Airflow for Data-Driven Policy Research at CSET with Jennifer Melot

support@contentallies.com (Astronomer) — Thu, 27 Feb 2025 13:15:00 -0500

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/

https://airflow.apache.org/

Zenodo -

https://zenodo.org/

OpenLineage -

https://openlineage.io/

Cloud Dataplex -

https://cloud.google.com/dataplex

#AI #Automation #Airflow #MachineLearning

Hybrid Testing Solutions for Autonomous Driving at Bosch with Jens Scheffler and Christian Schilling

support@contentallies.com (Astronomer) — Thu, 13 Feb 2025 00:10:00 -0500

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/

https://airflow.apache.org/

https://kubernetes.io

GitHub -

https://github.com

Edge Executor -

https://airflow.apache.org/docs/apache-airflow/stable/core-concepts/executor/index.html

#AI #Automation #Airflow #MachineLearning

Overcoming Airflow Scaling Challenges at Monzo Bank with Jonathan Rainer

support@contentallies.com (Astronomer) — Thu, 06 Feb 2025 22:09:00 -0500

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/

https://airflow.apache.org/

BigQuery -

https://airflow.apache.org/docs/apache-airflow-providers-google/stable/operators/cloud/bigquery.html

https://kubernetes.io/

#AI #Automation #Airflow #MachineLearning

Orchestrating Analytics and AI Workflows at Telia with Arjun Anandkumar

support@contentallies.com (Astronomer) — Thu, 30 Jan 2025 00:00:00 -0500

The 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/

Medallion Architecture by Databricks -

https://airflow.apache.org/

Cosmos by Astronomer -

https://www.astronomer.io/cosmos/

Terraform -

https://www.terraform.io/

https://www.databricks.com/glossary/medallion-architecture

#AI #Automation #Airflow #MachineLearning

The Role of Airflow in Finance Transformation at Etraveli Group with Mihir Samant

support@contentallies.com (Astronomer) — Thu, 23 Jan 2025 05:47:09 -0500

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/

Vasantha Kosuri-Marshall -

https://airflow.apache.org/

Docker -

https://www.docker.com/

Databricks -

https://www.databricks.com/

#AI #Automation #Airflow #MachineLearning

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

support@contentallies.com (Astronomer) — Thu, 16 Jan 2025 00:15:00 -0500

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:

https://www.linkedin.com/in/vasantha-kosuri-marshall-0b0aab188/

Google Cloud Platform (GCP) -

https://airflow.apache.org/

https://cloud.google.com/

Ford Motor Company | LinkedIn -

https://www.linkedin.com/company/ford-motor-company/

Ford Motor Company | Website -

https://www.ford.com/

https://www.astronomer.io/

#AI #Automation #Airflow #MachineLearning

Powering Finance With Advanced Data Solutions at Ramp with Ryan Delgado

support@contentallies.com (Astronomer) — Fri, 10 Jan 2025 11:15:02 -0500

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/

https://airflow.apache.org/

Snowflake -

https://www.snowflake.com/

ClickHouse -

https://clickhouse.com/

dbt -

https://www.getdbt.com/

#AI #Automation #Airflow #MachineLearning

Exploring the Power of Airflow 3 at Astronomer with Amogh Desai

support@contentallies.com (Astronomer) — Fri, 20 Dec 2024 13:41:00 -0500

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/

Apache Airflow GitHub Repository -

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

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

#AI #Automation #Airflow #MachineLearning

Using Airflow To Power Machine Learning Pipelines at Optimove with Vasyl Vasyuta

support@contentallies.com (Astronomer) — Thu, 12 Dec 2024 11:28:03 -0500

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/

https://airflow.apache.org/

Snowflake -

https://www.snowflake.com/

Datadog -

https://www.datadoghq.com/

https://astronomer.typeform.com/airflowsurvey24

#AI #Automation #Airflow #MachineLearning

Maximizing Business Impact Through Data at GlossGenius with Katie Bauer

support@contentallies.com (Astronomer) — Thu, 05 Dec 2024 04:19:38 -0500

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/

https://airflow.apache.org/

DBT -

https://www.getdbt.com/

Cosmos -

https://cosmos.apache.org/

https://astronomer.typeform.com/airflowsurvey24

#AI #Automation #Airflow #MachineLearning

Optimizing Large-Scale Deployments at LinkedIn with Rahul Gade

support@contentallies.com (Astronomer) — Mon, 02 Dec 2024 13:03:19 -0500

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/

https://airflow.apache.org/

Open Policy Agent (OPA) -

https://kubernetes.io/

https://www.openpolicyagent.org/

Backstage -

https://backstage.io/

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

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

support@contentallies.com (Astronomer) — Thu, 14 Nov 2024 01:00:00 -0500

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/

https://airflow.apache.org/

Airflow Summit -

https://airflowsummit.org/

Uber -

https://www.uber.com/tw/en/

https://astronomer.typeform.com/airflowsurvey24

#AI #Automation #Airflow #MachineLearning

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

support@contentallies.com (Astronomer) — Thu, 07 Nov 2024 03:27:47 -0500

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/

https://airflow.apache.org/

Google Cloud -

https://cloud.google.com/

dbt -

https://www.getdbt.com/

https://astronomer.typeform.com/airflowsurvey24

#AI #Automation #Airflow #MachineLearning

Inside Airflow 3: Redefining Data Engineering with Vikram Koka

support@contentallies.com (Astronomer) — Thu, 31 Oct 2024 06:01:00 -0400

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/

The Apache Software Foundation LinkedIn -

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

https://www.linkedin.com/company/the-apache-software-foundation/

Apache Airflow LinkedIn -

https://www.linkedin.com/company/apache-airflow/

https://airflow.apache.org/

The Apache Software Foundation -

https://www.astronomer.io/

https://www.apache.org/

Join the Airflow slack and/or Dev list -

https://airflow.apache.org/community/

https://astronomer.typeform.com/airflowsurvey24

#AI #Automation #Airflow #MachineLearning

Building a Data-Driven HR Platform at 15Five with Guy Dassa

support@contentallies.com (Astronomer) — Thu, 24 Oct 2024 06:00:00 -0400

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/

https://airflow.apache.org/

MLflow -

https://mlflow.org/

DBT -

https://www.getdbt.com/