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 Data teams everywhere face the same problem: they're forcing ML models, streaming data, and real-time processing through orchestration tools built for simple ETL. The result? Inflexible infrastructure that can't adapt to different workloads. That's why Cash App and Cisco rely on Prefect. Cash App's fraud detection team got what they needed - flexible compute options, isolated environments for custom packages, and seamless data exchange between workflows. Each model runs on the right infrastructure, whether that's high-memory machines or distributed compute. Orchestration is the foundation that determines whether your data team ships or struggles. ETL, ML model training, AI Engineering, Streaming - Prefect runs it all from ingestion to activation in one platform. Whoop and 1Password also trust Prefect for their data operations. 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Start building at MongoDB.com/Build 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 Email 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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