ABOUT THIS PODCAST 🔗
This show goes behind the scenes for the tools, techniques, and difficulties associated with the discipline of data engineering. Databases, workflows, automation, and data manipulation are just some of the topics that you will find here.
Hosts:
Update frequency:
every 16 days
Average audio length:
54 minutes
Guest interviews
Has sponsors
English
United States
518 episodes
since Jan. 8, 2017
episodic
AUDIENCE OF THIS PODCAST 🔗
~90.17% of listeners are from United States.
🇺🇸
US
90.17%
🇯🇵
JP
3.74%
🇮🇪
IE
0.58%
🇦🇺
AU
0.57%
🇸🇪
SE
0.53%
🇬🇧
GB
0.48%
🇹🇭
TH
0.44%
🇵🇭
PH
0.29%
🇨🇦
CA
0.29%
🇮🇳
IN
0.23%
🇦🇷
AR
0.21%
🇹🇼
TW
0.21%
🇩🇪
DE
0.21%
🇰🇬
KG
0.16%
🇻🇳
VN
0.16%
🇪🇪
EE
0.14%
🇪🇸
ES
0.14%
🇨🇿
CZ
0.12%
🇫🇷
FR
0.10%
Others
1.23%
* Data source: directly measured on Listen Notes. real-time
LATEST EPISODE 🔗
SummaryIn this episode Christopher Doidge talks about his Agile Ledger Architecture (ALA) approach to data warehousing and how it aims to reduce data debt while shortening the path from raw data to trustworthy business insight. Christopher explained that ALA is not a replacement for existing wareho…
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Search past episodes of Data Engineering Podcast.
PREVIOUS EPISODES 🔗
SummaryIn this episode Soham Azumdar, co-founder and CEO of Wisdom.ai, talks about what “context” really means in data engineering and AI systems. He explores why context has become such an overloaded term, spanning everything from semantic layers and data catalogs to tribal knowledge, query logs, …
Summary In this episode Yetunde Dada discusses Otto, Astronomer’s AI agent for Airflow, and the broader challenge of making agentic tooling actually useful for data engineers. She explored why generic coding assistants often fall short in data workflows, how Otto adds the missing context aroun…
Summary In this episode Ragnor Comerford talks about OmniGraph, a lakehouse-native graph storage layer designed around the needs of agentic systems. He explores how graphs are primarily a semantic model for representing the world, rather than just a specialized engine for traversal workloads, …
Summary In this episode Prukalpa Sankar, co-founder of Atlan, talks about what it takes to build a “context flywheel” for AI agents in data-intensive organizations. She explained why model intelligence alone isn’t enough to make AI useful in production, and how real performance depends on cont…
Summary In this episode Jevin Maltais talks about the practical realities of building reliable, product-focused streaming systems with Kafka. Jevin shares lessons from roles at Zapier, Humi, and Clio, where real-time synchronization, customer data unification, and document sync at scale highli…
Summary In this episode Shravan Gunda, founder and CEO of Kaarvi AI, talks about building an AI-native, agent-driven data platform designed to eliminate the janitorial work that consumes most data teams. He explores Kaarvi’s multi-agent architecture that runs queries across seven LLMs in paral…
SummaryIn this episode Weimo Liu, co‑founder of PuppyGraph, talks about the engineering behind their “zero-copy” graph querying engine for lakehouse and database sources. He explores how PuppyGraph lets you run Cypher and Gremlin traversals and graph algorithms directly on data in Iceberg, Delta, H…
SummaryIn this episode Robert Nishihara, co-founder of Anyscale and co-creator of Ray, talks about maximizing hardware utilization for AI and data-intensive workloads. He explores Ray’s evolution alongside Kubernetes and PyTorch, and why consolidation at these layers has enabled a new generation of…
Summary In this episode, I sit down with Gleb Mezhanskiy, CEO and co-founder of Datafold, to explore how agentic AI is reshaping data engineering. We unpack the leap from chat-assisted coding to truly agentic workflows where AI not only writes SQL and dbt models but also executes queries, debu…
Disclaimer: The podcast and artwork embedded on this page are from Tobias Macey, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
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