Engineering

The Data Plane Is Yours: Dagster+ Hybrid on Kubernetes and Azure

September 8, 2026

The Data Plane Is Yours: Dagster+ Hybrid on Kubernetes and Azure

This article draws on a Dagster+ Hybrid deployment on Kubernetes and Azure at a very large enterprise. It covers the work outside the Helm chart: workload ownership, isolation, networking, identity, sizing, and capacity.

James Dzidek

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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

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

November 17, 2025

When (and When Not) to Optimize Data Pipelines

Engineers often optimize the wrong parts of their pipelines, here's a profiling-first framework to identify real bottlenecks and avoid the premature optimization trap.

Alex Noonan

Introducing Our New eBook: Scaling Data Teams

November 5, 2025

Introducing Our New eBook: Scaling Data Teams

Learn how real data teams, from solo practitioners to enterprise-scale organizations, build in Dagster’s new eBook, Scaling Data Teams.

Dennis Hume

September 30, 2025

Building a Better Lakehouse: From Airflow to Dagster

How I took an excellent lakehouse tutorial and made it even better with modern data orchestration

Eric Thomas

September 25, 2025

Designing User-Friendly Dagster Components

The difference between components that thrive and components that collect digital dust? User experience design.

Dennis Hume

August 22, 2025

dbt Fusion Support Comes to Dagster

Learn how to use the beta dbt Fusion engine in your Dagster pipelines, and the technical details of how support was added

Colton Padden

What CoPilot Won’t Teach You About Python (Part 2)

August 20, 2025

What CoPilot Won’t Teach You About Python (Part 2)

Explore another set of powerful yet overlooked Python features—from overload and cached_property to contextvars and ExitStack

Dennis Hume