Blog

February 6, 2026

Evaluating Skills

We built a light weight evaluation framework to quantiatively measure the effectiveness of the Dagster Skills, and these are our findings.

Colton Padden

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Great Infrastructure Needs Great Stories: Designing our Children’s Book

February 5, 2026

Great Infrastructure Needs Great Stories: Designing our Children’s Book

We set out to explain Dagster assets in the simplest possible way: as living characters that wait, react, and change with their dependencies. By designing a children’s book with warmth, visuals, and motion, we rediscovered what makes assets compelling in the first place.

Dennis Hume

February 3, 2026

Closing the DataOps Loop: Why We Built Compass for Dagster+

Detection isn't the bottleneck anymore. Understanding is. Compass closes the loop by turning Dagster+ operational data into a conversation.

Alex Noonan

Pytest for Agent-Generated Code: Concrete Testing Strategies to Put Into Practice

January 26, 2026

Pytest for Agent-Generated Code: Concrete Testing Strategies to Put Into Practice

When agents write tests, intent matters as much as correctness. By defining clear testing levels, preferred patterns, and explicit anti-patterns, we give agents the structure they need to produce fast, reliable Pytest suites that scale with automation.

Dennis Hume

January 21, 2026

Dagster + Snowflake: Building Production AI Pipelines with Cortex

Snowflake handles AI compute while Dagster handles orchestration, observability, and the operational patterns that turn AI experiments into reliable production pipelines.

Alex Noonan

January 15, 2026

Your GTM Data, Finally Untangled

Compass now connects directly to your go-to-market tools, letting you ask questions about pipeline, ad spend, and sales conversations in Slack without exporting CSVs or waiting on the data team.

Alex Noonan

Dignified Python: 10 Rules to Improve your LLM Agents

January 9, 2026

Dignified Python: 10 Rules to Improve your LLM Agents

Modern LLMs generate patterns, not principles. Dignified Python gives agents the intent they lack, ensuring code is explicit, consistent, and engineered with care. Here are ten rules from our Claude prompt.

Colton Padden

January 7, 2026

Evaluating Model Behavior Through Chess

Benchmarks measure outcomes, not behavior. By letting AI models play chess in repeatable tournaments, we can observe how they handle risk, repetition, and long-term objectives, revealing patterns that static evals hide.

Dennis Hume

January 6, 2026

How to Enforce Data Quality at Every Stage: A Practical Guide to Catching Issues Before They Cost You

This post gives you a framework for enforcing data quality at every stage so you catch issues early, maintain trust, and build platforms that actually work in production.

Alex Noonan

January 5, 2026

When Sync Isn’t Enough

This post introduces a custom async executor for Dagster that enables high-concurrency fan-out, async-native libraries, and incremental adoption, without changing how runs are launched or monitored.

Steven Ayers

December 22, 2025

How to Build a Data Platform That Actually Scales

Most teams build data platforms reactively when you should be architecting one that scales with your business, not against it.

Alex Noonan

Orchestrating Nanochat: Deploying the Model

December 16, 2025

Orchestrating Nanochat: Deploying the Model

Once the model is trained, the final step is getting it into users’ hands. This guide walks through turning your model into a fast, reliable RunPod endpoint—complete with orchestration and automated updates from Dagster.

Dennis Hume

December 15, 2025

Data Ingestion Patterns: When to Use Push, Pull, and Poll (With Real Examples)

A practical guide to choosing between push, pull, and poll data ingestion patterns. With real Dagster code examples to help you build reliable, maintainable pipelines.

Alex Noonan

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