על אודות פודקאסט זה 🔗
Welcome to The Data Flowcast: Mastering Apache Airflow ® for Data Engineering and AI— the podcast where we keep you up to date with insights and ideas propelling the Airflow community forward.
Join us each week, as we explore the current state, future and potential of Airflow with leading thinkers in the community, and discover how best to leverage this workflow management system to meet the ever-evolving needs of data engineering and AI ecosystems.
Podcast Webpage: https://www.astronomer.io/podcast/
Join us each week, as we explore the current state, future and potential of Airflow with leading thinkers in the community, and discover how best to leverage this workflow management system to meet the ever-evolving needs of data engineering and AI ecosystems.
Podcast Webpage: https://www.astronomer.io/podcast/
מנחים:
תדירות עדכון:
weekly
אורך אודיו ממוצע:
27 minutes
אנגלית
ארצות הברית
121 פרקים
מאז 19 בינואר 2018
episodic
הפרק החדש ביותר 🔗
At Lyft, a small data engineering team supports dozens of scientists and analysts who tune driver pay configurations on demand, all under SOX controls. In this episode, Ken Obata, Senior Software Engineer at Lyft, joins Kenten Danas to walk through how his team built self-service Dags with an even…
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Running Airflow with a one-person data engineering team means every decision about cost, tooling, and best practices has to earn its keep. In this episode, Kenten sits down with Aniruddha Sengupta, Staff Data Engineer at Insignis, to talk through how the UK cash-savings fintech runs a low-cost, sc…
Scaling Airflow to over 1,000 active DAGs on a team of 3-4 engineers takes structure. In this episode, Kenten Danas talks with Matt Stavinga, Data Engineer at Idelic (now part of Descartes), about how his team standardized DAG creation and logging, built a Jenkins-driven testing workflow with per-…
Running LLMs over sensitive data means external APIs are off the table, but open-weight models still need reproducibility, validation, and a paper trail before downstream systems can depend on their output. In this episode, Kenten is joined by Chhayank Jain, Senior ML Engineer at Visa, to talk thr…
Security is one of the least visible parts of Airflow for most users, but it shapes what features teams can turn on in production. In this episode, Kenten Danas talks with Anish Giri, Senior Software Engineer and active Apache Airflow contributor focused on security, about three recent contributio…
Orchestrating data across more than 30 companies means most Airflow users on the platform aren't data engineers. In this episode, Kenten Danas talks with Lucas Trubiano, Data Engineer at itti, the technology company within Grupo Vázquez in Paraguay. Lucas walks through the custom YAML framewo…
Manufacturing data pipelines can't afford silent failures. When quality decisions and shop-floor visibility depend on Airflow, a broken DAG at midnight can cost real money. In this episode, Kenten Danas talks with Kumuda Sreenivasa, Founder of Receitly and Senior Data Architect at ATC Drivetr…
Strict sequential execution across DAGs sounds simple until you have scheduled pipelines and event-driven pipelines writing to the same MongoDB collections. Ivana Isailovic, Senior Big Data Engineer at Synechron, joins Marc Lamberti to walk through the three-layer DAG architecture her team built t…
Financial data pipelines have to be right the first time. On this episode, Kenten sits down with [Antonello Benedetto](linkedin.com/in/anbento4), Staff Data Engineer at Wise, to talk about how the central data and analytics engineering team runs Airflow for critical financial datasets, tiers pipel…
Running a travel platform means dealing with fast-moving inventory, real-time fraud detection, and heavy performance marketing attribution, all while keeping data trustworthy for a data-driven org. In this episode, Mrinalini Singh, Data Platform Engineer at [Headout](headout.com), walks through ho…
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