Digging Into Dagster: An Opinionated Open Source Framework For Data Orchestration

Summary

Data applications are complex and continually evolving, often requiring collaboration across multiple teams. In order to keep everyone on the same page a high level abstraction is needed to facilitate a cross-cutting view of the data orchestration across integration, transformation, analytics, and machine learning. Dagster is an innovative new framework that leans on the power and flexibility of Python to provide an extensible interface to the complete lifecycle of data projects. In this episode Nick Schrock explains how he designed the Dagster project to allow for integration with the entire data ecosystem while providing an opinionated structure for connecting the different stages of computation. He also discusses how he is working to grow an open ecosystem around the Dagster project, and his thoughts on building a sustainable business on top of it without compromising the integrity of the community. This was a great conversation about playing the long game when building a business while providing a valuable utility to a complex problem domain.

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• Your host as usual is Tobias Macey and today I’m interviewing Nick Schrock about Dagster, an open source data orchestrator for powering data engineering, analytics, and machine learning

Interview

• Introductions
• How did you get introduced to Python?
• Can you start by describing what Dagster is and how it got started?
• What are the most common difficulties that organizations face when working with data projects?
  • How does Dagster help in addressing those challenges?
• There are a number of workflow orchestration platforms, spanning a few generations of tooling. What do you see as the defining characteristics of the various options, and how does Dagster fit in that ecosystem?
• What are the assumptions that you made at the start of building Dagster and how have they been challenged, updated, or invalidated over the past year of working with end users?
• How are the internals of Dagster implemented?
  • How has the design changed or evolved since you first began working on it?
• For someone who is building on top of Dagster, what is their workflow from first steps through to production?
• What are your guiding principles for desigining the user facing API?
• What are the available extension points for Dagster?
• What was your reason for implementing Dagster as a Python framework?
  • With the benefit of hindsight, would you make the same decision today?
• What are some of the most interesting, innovative, or unexpected ways that you have seen Dagster used?
• What are the most interesting, unexpected, or challenging lessons that you have learned while building Dagster and working to grow its ecosystem?
• When is Dagster the wrong choice?
• As you continue to build Dagster, what is your vision for it and its ecosystem?
  • What are the next steps that you are taking to achieve that vision?

Keep In Touch

• @schrockn on Twitter
• schrockn on GitHub
• LinkedIn

Picks

• Tobias
  • Caddy web server
• Nick
  • Black code formatter

Closing Announcements

• Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.
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Links

• Dagster
• Elementl
• IronPython
• Fluent Python
• GraphQL
• Maslow’s Hierarchy of Needs
• Hierarchy of Data Needs
• DAG == Directed Acyclic Graph
• Informatica
• Airflow
• Luigi
• Dagster Config Schema
• Dask
  • Data Engineering Podcast Episode
  • Coiled Episode
• gRPC
• MyPy
  • Podcast Episode
• Data Lineage
• Pandas
  • Podcast Episode
• Amundsen
  • Podcast Episode
• DataHub
  • Podcast Episode
• Gatsby.js
• Panama Papers
• Mode Analytics
  • Podcast Episode
• Papermill
  • Podcast Episode
• DBT
  • Podcast Episode
• Databricks
• Tobias’ Dagster Repository

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA