Dagster: The Data Orchestrator

medium.com

23 points·schrockn··3 comments

For anyone who has spent significant time with data and ML pipelines, read posts about Dagster. Even if you don't end up using it in your pipeline, it is full of well articulated and extremely valuable lessons about how to manage the problems and complexity of data applications. I can't recommend reading about dagster enough!

The software itself has matured significantly in the last year as well - highly recommend taking a look at this if you're building new pipelines or have a need to upgrade existing ones.

After playing around with this tool a bit in the past I can't seem to understand what it's trying to solve. It comes across as a side project that VCs happened to throw money at.

hey bserial, I'm part of the team working on Dagster.

While there are many things we're working on, there are 3 goals that got me excited about working on this system:

1. Local development: most modern workflow orchestration systems don't have a good local development story. We want to provide a seamless end-to-end dev experience from your laptop to CI to dev to prod for authoring data workflows.

2. Complexity: the Airflow deployments I've worked on or otherwise encountered have hundreds of DAGs and thousands of tasks scheduled on an hourly or daily cadence. We aim to provide abstractions to better support managing and wrangling that complexity.

3. Testability: Most modern data platforms are poorly tested. Many orchestration systems, like Airflow, tend to hardcode deployment concerns the business logic, e.g. EmrAddStepsOperator. With Dagster, we aim to separate the business logic from environmental concerns to make it easy to swap out an external resource implementation for a mock, dev version, etc.

Hope that makes sense!

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