Christian Minich

* in the onboarding docs and guides we went from offering a single blessed path 0 (!!) times, compared with 19 times in the current docs.

If you’ve tried out Dagster before and found the learning curve a little too steep, take another look, I think you’ll be surprised at how much easier it is

3/3

* Huge improvements to the core framework, including the dg cli to create, validate, and deploy assets and Components, Components for a YAML DSL, the create-dagster CLI to generate new Dagster projects with defined structure).
* Docs that went from un-opinionated to Very Opinionated

2/3

Looking back on my 2 years (!!) at Dagster Labs, the thing about Dagster that improved the most is having Opinions about the best way to do things.

We’ve baked those Opinions throughout our docs and right into the heart of the Dagster library. Since I started, we have:

1/3

Why I Now Use uv for Everything Python uv replaces pip, pyenv, and Poetry in one tool — managing Python versions, dependencies, and scripts with a single fast binary.

Give it a read if you’re uv-curious, or if you already use it you might learn a thing or two:

#dataBS
2/2

www.realcloudnative.com/why-i-now-us...

The LinkedIn algorithm of all things heard my cry for a great uv + python skill, and while it didn’t quite deliver that, it did send me this incredible blog by Adriaan de Jonge that will now be my go to on explaining what problems uv solves and why it’s so useful.

1/2

Oh yeah if you don’t create a lot of envs and don’t update them the speed is absolutely not a huge deal.

For iteration and prod deployments (eg the joy that is slow Docker builds) it’s a godsend

The other sick feature is uv run —with requests script.py, which runs that script after installing the library in an ephemeral virtual env (or you can provide as special metadata in a file). Soo useful for utility scripts

You can create virtual envs for a folder outside of that folder, and then make sure you’re pointing at the correct one / activating the right one in the workflow.

Oh very interesting. I guess this is one reason why this is a Hard Problem, so many different ways of working!

I do loads of new environments for customer / prospects / demos and that works marvelously.

What went wrong? I did the same and never looked back so curious what made our experiences so different

My kingdom for a Skill that uses uv to install Python and manage virtual environments.

Like linters and formatters take the discussion out of style questions, can we take the discussion and questions out of who and how to manage Python and virtual environments?

Joining the resistance, one Claude Code session at a time

Claude code for iOS doesn’t have the plugin system but since plugins are just plain text you can ask Claude Code to clone the repo and copy the files to .claude/ and it just works!

Fun when things feel hackable like that

Oh yeah! I didn’t post this but I also do a this week I learned async thread. Best ones are the simple ones.

When I led an analytics eng team I abolished standup and replaced it with a “questions and blockers” meeting where everyone joined and helped each other

Second, I have a weekly Slack thread where we each share feedback on 1-2 calls from each other. The good, the bad, the ugly. No ego, everyone has great days and bad days. I’ve learned so much about what’s working, what’s not, what I should try next, and it’s very, very fun.

3/3

Two things I started doing recently to help with this. First, in our weekly team meetings our first 10 minutes is devoted to the “brag corner” — SEs brag for themselves or for someone else! We share our wins and put it in a running doc.

2/3

I’ve led two remote teams where the work is similar but you do it independently (analytics engineering and now sales engineering). It’s really important to share what’s working and what’s not, but it’s hard if you don’t have “water cooler” conversations. 1/3

The best tools, models, and workflows will achieve large adoption, and the time we all spent yak shaving will be wasted, right?

Wrong. Think of yourself as a VC. ~75% of your bets will be ROI negative, but the 25% that do will swamp the bad bets.

Sunk cost fallacy is a fallacy for a reason

I see AI-forward teams spending a ton of time building bespoke tools to help them develop more effectively using agents (see: proliferation of CLIs to work with git worktrees), trying out different models, testing our new workflows.

Read how Magenta (👋 @geoheil.com and @milicevica23.bsky.social ) uses Dagster+ to feel a bit of that joy: dagster.io/customers/ho...

I love working with large enterprise teams. That may sound funny, but these teams are solving the most difficult chalenges, and being able to let data platform and pipeline developers have nice things _and_ have a secure, scaled data platform in a regulated industry is so satisfying.

#dataBS

1/2

Not a CTO but have had similar feelings anytime I review a legal document

We use Vanta for this and holy cow it saves a lot of time and keeps things standard

Not my blog post, but Robin chose Dagster to orchestrate. I put the 😁 because I work for Dagster Labs as a sales engineer. Wasn’t the main point of the article but I did want to call it out since I work there and since it has a great integration with dbt

GitHub - danielpodrazka/dagster-document-intelligence: Dagster pipeline that ingests K-1 tax documents, detects and redacts PII, extracts structured financial data with AI, and generates PDF reports —... Dagster pipeline that ingests K-1 tax documents, detects and redacts PII, extracts structured financial data with AI, and generates PDF reports — with a React audit dashboard. - danielpodrazka/dags...

Another day, another project built using the new Dagster skills! This one by Daniel Podrazka.

My first job out of college was similar analysis for the Fed. Incredible to see a similar pipeline to my old full time job built with AI using AI in it

Link: github.com/danielpodraz...

#dataBS

This is probably the blog I would send to someone to introduce why dbt to any technical person who is new to it.

So good

A Deep Dive into ADBC Driver Optimization What it takes to achieve good performance across different data systems

Love me a good optimization whodunnit

columnar.tech/blog/adbc-dr...

This is a wonderful introduction to dbt for those who come from data engineering and are dbt skeptical.

The best part is that Robin comes at it from a place of curiousity and willingness to learn, and comes away excited!

And as a bonus, great choice of orchestrator 😁

I use skills almost daily to generate brand new demos and projects. With the right context they’re extraordinarily good at greenfield too

It’s gotten dramatically better since I started:

* more opinionated docs and project structure
* dg cli and create-dagster cli
* components system

And now with skills getting started the recommended way is so much easier