Engineering

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

August 8, 2025

Dagster’s MCP Server

We are announcing the release of our MCP server, enabling AI assistants like Cursor to seamlessly integrate with Dagster projects through Model Context Protocol, unlocking composable workflows across your entire data stack.

August 7, 2025

Untangling Python Packages Part 2

A deep dive into how Dagster leverages pyproject.toml for modern Python packaging, from project metadata and dependencies to build systems and development tooling.

July 23, 2025

What CoPilot Won’t Teach You About Python (Part 1)

Advanced Python features that AI agents may miss

March 4, 2025

Building with Dagster vs Airflow

Rebuilding Airflow's tutorial in Dagster

January 24, 2025

AI Reference Architectures

Guide to the some common AI Architectures patterns with Dagster

January 24, 2025

From Prototype to Production: Building AI Products That Scale with Dagster

Modern AI development requires different patterns than traditional software. By combining familiar engineering practices with new approaches for handling the probabilistic nature of AI, teams can successfully scale their AI products into production.

December 2, 2024

Interactive Debugging With Dagster and Docker

Step-by-step guide to debugging Dagster code directly in Docker, bridging the gap between development and deployment.

November 14, 2024

Bridging Business Intelligence and Data Orchestration with Dagster + Sigma

Break down the silos between data engineering and BI tools

October 28, 2024

AI's Long-Term Impact on Data Engineering Roles

Expectations for Data Engineering will rapidly inflate; the nature of the work will change.

October 14, 2024

From Chaos to Control: How Dagster Unifies Orchestration and Data Cataloging

Navigate complex data environments more effectively, and ensure that valuable data assets are easily discoverable and usable.

October 3, 2024

10 Reasons Why No-Code Solutions Almost Always Fail

No-code solutions sound easy – until they aren’t. Here’s why they often fail and what you can do about it for your data engineering.

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