Summary
In this solo episode of the Data Engineering Podcast, host Tobias Macey reflects on how AI has transformed the practice and pace of data engineering over time. Starting from its origins in the Hadoop and cloud warehouse era, he explores the discipline's evolution through ML engineering and MLOps to today's blended boundaries between data, ML, and AI engineering. The conversation covers how unstructured data is becoming more prominent, vectors and knowledge graphs are emerging as key components, and reliability expectations are changing due to interactive user-facing AI. The host also delves into process changes, including tighter collaboration, faster dataset onboarding, new governance and access controls, and the importance of treating experimentation and evaluation as fundamental testing practices.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management
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Your host is Tobias Macey and today I'm interviewing reflecting about the increasingly blurry boundaries between data engineering and AI engineering

Interview

Introduction
I started this podcast in 2017, right when the term "Data Engineer" was becoming widely used for a specific job title with a reasonably well-understood set of responsibilities. This was in response to the massive hype around "data science" and consequent hiring sprees that characterized the mid-2000s to mid-2010s. The introduction of generative AI and AI Engineering to the technical ecosystem is changing the scope of responsibilities for data engineers and other data practitioners. Of note is the fact that:
AI models can be used to process unstructured data sources into structured data assets
AI applications require new types of data assets
The SLAs for data assets related to AI serving are different from BI/warehouse use cases
The technology stacks for AI applications aren't necessarily the same as for analytical data pipelines
Because everything is so new there is not a lot of prior art, and the prior art that does exist isn't necessarily easy to find because of differences in terminology
Experimentation has moved from being just an MLOps capability into being a core need for organizations

Contact Info

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

AI Engineering Podcast

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

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