Summary

Despite the fact that businesses have relied on useful and accurate data to succeed for decades now, the state of the art for obtaining and maintaining that information still leaves much to be desired. In an effort to create a better abstraction for building data applications Nick Schrock created Dagster. In this episode he explains his motivation for creating a product for data management, how the programming model simplifies the work of building testable and maintainable pipelines, and his vision for the future of data programming. If you are building dataflows then Dagster is definitely worth exploring.

Announcements

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Your host is Tobias Macey and today I’m interviewing Nick Schrock about Dagster, an open source system for building modern data applications

Interview

Introduction
How did you get involved in the area of data management?
Can you start by explaining what Dagster is and the origin story for the project?
In the tagline for Dagster you describe it as "a system for building modern data applications". There are a lot of contending terms that one might use in this context, such as ETL, data pipelines, etc. Can you describe your thinking as to what the term "data application" means, and the types of use cases that Dagster is well suited for?
Can you talk through how Dagster is architected and some of the ways that it has evolved since you first began working on it?
- What do you see as the current industry trends that are leading us away from full stack frameworks such as Airflow and Oozie for ETL and into an abstracted programming environment that is composable with different execution contexts?
- What are some of the initial assumptions that you had which have been challenged or updated in the process of working with users of Dagster?
For someone who wants to extend Dagster, or integrate it with other components of their data infrastructure, such as a metadata engine, what interfaces do you provide for extensibility?
For someone who wants to get started with Dagster can you describe a typical workflow for writing a data pipeline?
- Once they have something working, what is involved in deploying it?
One of the things that stands out about Dagster is the strong contracts that it enforces between computation nodes, or "solids". Why do you feel that those contracts are necessary, and what benefits do they provide during the full lifecycle of a data application?
Another difficult aspect of data applications is testing, both before and after deploying it to a production environment. How does Dagster help in that regard?
It is also challenging to keep track of the entirety of a DAG for a given workflow. How does Dagit keep track of the task dependencies, and what are the limitations of that tool?
Can you give an overview of where you see Dagster fitting in the overall ecosystem of data tools?
What are some of the features or capabilities of Dagster which are often overlooked that you would like to highlight for the listeners?
Your recent release of Dagster includes a built-in scheduler, as well as a built-in deployment capability. Why did you feel that those were necessary capabilities to incorporate, rather than continuing to leave that as end-user considerations?
You have built a new company around Dagster in the form of Elementl. How are you approaching sustainability and governance of Dagster, and what is your path to sustainability for the business?
What should listeners be keeping an eye out for in the near to medium future from Elementl and Dagster?
- What is on your roadmap that you consider necessary before creating a 1.0 release?

Contact Info

@schrockn on Twitter
schrockn on GitHub
LinkedIn

Parting Question

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

Links

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

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