Engineering

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

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February 5, 2024

Thinking in Assets When Building Data Pipelines

How to develop data pipelines using Software-defined Assets.

Tim Castillo

December 4, 2023

How Dagster Labs runs Dagster: Open-Sourcing our Own Pipelines

A technical deep dive into the patterns and implementations of the Dagster Open Platform using our open-sourced code and dbt models.

Tim Castillo

November 29, 2023

Scaling Dagster’s DAG Visualization to Handle Tens of Thousands of Assets

How the Dagster frontend team rapidly scaled Dagster’s DAG visualization for enterprise-sized data asset graphs.

Jordan Sanders

November 20, 2023

High-performance Python for Data Engineering

Learn how to optimize your Python data pipeline code to run faster with our high-performance Python guide for data engineers.

Elliot Gunn

October 20, 2023

CI/CD and Data Pipeline Automation (with Git)

Learn how to automate data pipelines and deployments by integrating Git and CI/CD in our Python for data engineering series.

Elliot Gunn

October 13, 2023

Introducing Dagster External Assets

Use Dagster’s External Assets feature for data observability, lineage, data quality, and cataloging while bringing your own orchestration and scheduling.

Nick Schrock

October 13, 2023

Introducing Dagster Pipes

A new protocol and toolkit for integrating and launching compute into remote execution environments from Dagster.

Nick Schrock

October 12, 2023

Stop Reinventing Orchestration: Embedded ELT in the Orchestrator

Solve data ingestion issues with Dagster's Embedded ELT feature, a lightweight embedded library.

Pedram Navid

October 9, 2023

Introducing Dagster Asset Checks

Deliver high-quality data with Dagster Asset Checks, the ability to embed data quality checks into your data pipeline.

Sandy Ryza

September 29, 2023

Write-Audit-Publish in data pipelines

We look at the write-audit-publish software design pattern used in ETL to ensure quality and reliability in data engineering workflows.

Elliot Gunn

September 4, 2023

Factory Patterns in Python

We explore design patterns — reusable solutions to common problems in software design — as used in data engineering, specifically factory patterns in Python.

Elliot Gunn

August 18, 2023

Building an Outbound Reporting Pipeline

Learn how to use data engineering patterns and Dagster’s dynamic partitioning to build an outbound email report delivery pipeline.

James Timmins

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