Nick Schrock, CTO and founder of Dagster Labs, shares his expertise on the transformational role of AI in data engineering. He discusses the importance of maintaining core data principles amid AI advancements and emphasizes the necessity of human oversight. Nick introduces Dagster's innovative components feature that modularizes data transformations, enhancing collaboration. The conversation also covers the balance between simplifying tools for non-technical users and maintaining customizability, addressing key challenges in the evolving data landscape.
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insights INSIGHT
AI's Disruptive But Augmenting Role
AI will bring massive productivity boosts but won't replace software engineers.
The impact on data engineering is beginning but profound change is coming soon.
insights INSIGHT
Core Data Engineering Stays Constant
Data engineering fundamentals remain the same in AI era; the shape evolves.
Data engineers transform raw data into knowledge to empower business decisions.
insights INSIGHT
Data Orchestration Trends Explored
Data platform teams increasingly build dynamic, configurable pipelines at higher abstraction levels.
Open table formats and lakehouse architectures become key industry trends against hyperscale vertical consolidation.
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Summary In this episode of the Data Engineering Podcast we welcome back Nick Schrock, CTO and founder of Dagster Labs, to discuss the evolving landscape of data engineering in the age of AI. As AI begins to impact data platforms and the role of data engineers, Nick shares his insights on how it will ultimately enhance productivity and expand software engineering's scope. He delves into the current state of AI adoption, the importance of maintaining core data engineering principles, and the need for human oversight when leveraging AI tools effectively. Nick also introduces Dagster's new components feature, designed to modularize and standardize data transformation processes, making it easier for teams to collaborate and integrate AI into their workflows. Join in to explore the future of data engineering, the potential for AI to abstract away complexity, and the importance of open standards in preventing walled gardens in the tech industry.
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 Nick Schrock about lowering the barrier to entry for data platform consumers
Interview
Introduction
How did you get involved in the area of data management?
Can you start by giving your summary of the impact that the tidal wave of AI has had on data platforms and data teams?
For anyone who hasn't heard of Dagster, can you give a quick summary of the project?
What are the notable changes in the Dagster project in the past year?
What are the ecosystem pressures that have shaped the ways that you think about the features and trajectory of Dagster as a project/product/community?
In your recent release you introduced "components", which is a substantial change in how you enable teams to collaborate on data problems. What was the motivating factor in that work and how does it change the ways that organizations engage with their data?
tension between being flexible and extensible vs. opinionated and constrained
increased dependency on orchestration with LLM use cases
reducing the barrier to contribution for data platform/pipelines
bringing application engineers into the mix
challenges of meeting users/teams where they are (languages, platform investments, etc.)
What are the most interesting, innovative, or unexpected ways that you have seen teams applying the Components pattern?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on the latest iterations of Dagster?
When is Dagster the wrong choice?
What do you have planned for the future of Dagster?
Data Engineering Podcast