Dagster's New Era: Modularizing Data Transformation in the Age of AI

Data Engineering Podcast

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?

Contact Info

• LinkedIn

Parting Question

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

Links

• Dagster+ Episode
• Dagster Components Slide Deck
• The Rise Of Medium Code
• Lakehouse Architecture
• Iceberg
• Dagster Components
• Pydantic Models
• Kubernetes
• Dagster Pipes
• Ruby on Rails
• dbt
• Sling
• Fivetran
• Temporal
• MCP == Model Context Protocol

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