Classifying a Million Snowflake Columns in 9 Days, Solo, with Dagster
data governance. I built a tiered AI classification system, human review workflow, and the Dagster orchestration that ties it all together in production in nine days.
Engineering
Showing 12 of 112 articles.
Classifying a Million Snowflake Columns in 9 Days, Solo, with Dagster
data governance. I built a tiered AI classification system, human review workflow, and the Dagster orchestration that ties it all together in production in nine days.
Operationalizing Data Orchestration: Best Practices for DevOps, Infra, and Code Locations
A complete guide with all the insights, tips, and some predictions for the data platform engineer, just like an Almanack provides, with practical information for daily life.
We Tried ty for Performance. It Found Real Bugs
We adopted Astral’s new Python type checker, ty, to speed up type checking in the Dagster monorepo. The performance gains were dramatic, but the bigger surprise was that ty caught real runtime bugs Pyright missed. Here’s what we learned migrating a large Python codebase incrementally to ty.
The Dagster Almanack: From Complexity to Composability
A complete guide with all the insights, tips, and some predictions for the data platform engineer, just like an Almanack provides, with practical information for daily life.
How to Orchestrate Across Multiple Databricks Workspaces Without Losing Your Mind
Once your pipelines span multiple Databricks workspaces, you're no longer orchestrating a single system you're coordinating a distributed one.
Monorepos, the hub-and-spoke model, and Copybara
How we configure Copybara for bi-directional syncing to enable a hub-and-spoke model for Git repositories
DataOps with Dagster: A Practical Guide to Building a Reliable Data Platform
DataOps is about building a system that provides visibility into what's happening and control over how it behaves
Announcing AI Driven Data Engineering
AI coding agents are changing how data engineers work. This Dagster University course shows how to build a production-ready ELT pipeline from prompts while learning practical patterns for reliable AI-assisted development.
Sample-Level Versioning for ML Pipelines with Dagster and Metaxy
Learn how Metaxy can be used to build multimodal data pipelines with sample-level granularity on Dagster
Evaluating Skills
We built a light weight evaluation framework to quantiatively measure the effectiveness of the Dagster Skills, and these are our findings.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Building a Better Lakehouse: From Airflow to Dagster
How I took an excellent lakehouse tutorial and made it even better with modern data orchestration
Designing User-Friendly Dagster Components
The difference between components that thrive and components that collect digital dust? User experience design.
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
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
Software-Defined Assets Explained
Software-Defined Assets are a new abstraction that allows data teams to focus on the end products, not just the individual tasks, in their data pipeline.
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.
Untangling Python Packages Part 1
Python's clean syntax makes it easy to jump into unfamiliar codebases, but this simplicity often masks the intricate world of packaging that confuses many developers.
A Practical Guide to Dagster Resources
How dependency injection and smart resource management can save your sanity (and your deployments)
How to Structure Your Dagster Project
Setting up your Dagster project the right way from day one saves you headaches later, makes your team more effective, and helps scale.
Code Location Best Practices
How to organize your code locations for clarity, maintainability, and reuse.
Dagster Pipes: Now available for TypeScript, Rust, and Java
Expanding Dagster pipes to support Typescript, Rust, and Java
Cut Through the Noise: Precision Data Management with Dagster's Asset Selection Framework
Data platforms can be complex, Dagster's understanding of Lineage makes it easy to get to whats important.
Accelerate Data Pipeline Development with Dagster Components
Introducing Dagster Components, a simplified approach to developing and managing your data pipelines
The Case for Dagster: Moving Beyond Airflow in the Modern Data Stack™
How we think about data orchestration needs to fundamentally change, and Dagster represents that shift in thinking.
Free Your Mind With Dagster
You need tools that handle the trivial stuff and give you, your team, and your company space to think and act decisively.
MS Fabric vs. Dagster: Why Your Architecture Choices Matter
The fundamental challenge facing data teams today is building scalable platforms that enable self-service for data consumers.
Dagster University Presents: Testing with Dagster
Learn best practices for writing Pythonic tests for Dagster.
Observability That Matters with Dagster+ Alerts
Broken pipelines are unavoidable. Catch problems as soon as they happen with the improved alerting suite in Dagster+.
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.
Interactive Debugging With Dagster and Docker
Step-by-step guide to debugging Dagster code directly in Docker, bridging the gap between development and deployment.
Bridging Business Intelligence and Data Orchestration with Dagster + Sigma
Break down the silos between data engineering and BI tools
AI's Long-Term Impact on Data Engineering Roles
Expectations for Data Engineering will rapidly inflate; the nature of the work will change.
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.
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.
Dagster Deep Dive Recap: Orchestrating Flexible Compute for ML with Dagster and Modal
Learn how to use Dagster and Modal to automate and streamline your machine learning model training and data processing.
The Rise of the Data Platform Engineer
How the next step in the evolution of the Data Engineering role requires a platform approach.
Dagster vs. Airflow
Get the tale of the tape between the two orchestration giants and see why Dagster stands tall as the superior choice.
What is Data Visibility?
The unseen data is often the deadliest. Here’s how to shine a light on it in your business.
Dagster Deep Dive Recap: Building a True Data Platform
Move past the MDS and build a data platform for observability, cost-efficiency, and top-tier orchestrating.
Dagster Deep Dive Recap: Evolution of the Data Platform
Dagster and SDF show how the power of two can connect local development and production orchestration.
Dagster Deep Dive Recap: Building Reliable Data Platforms
Explore the importance of data quality and learn strategies for integrating quality checks using Dagster.
Enabling Data Quality with Dagster and Great Expectations
Use Dagster and GX to improve data pipeline reliability without writing custom logic for data testing.
ELT Options in Dagster
Why running data ingestion jobs straight from the orchestrator is often a preferred approach.
Building Cost Effective AI Pipelines with OpenAI, LangChain, and Dagster
Leverage the power of LLMs while keeping the costs in check using the Dagster OpenAI integration.
Unlocking Flexible Pipelines: Customizing the Asset Decorator
Use Asset Factories within Dagster to streamline data asset creation, promote code reusability, and maintain data engineering workflows.
The Data Engineering Impedance Mismatch
A case for asset-oriented over workflow-oriented in data orchestration.
Expanding the Dagster Embedded ELT Ecosystem with dltHub for Data Ingestion
We now have an officially supported dlt integration.
Exploring The Data Engineering Lifecycle
Learn the fundamentals of a healthy data engineering lifecycle to optimize pipeline and asset production.
New Dagster Integration: Include OpenAI Calls Into Your Data Pipelines
The new dagster-openai integration lets you tap into the power of LLMs in a cost-efficient way.
Balancing the Data Scales: Centralization vs. Decentralization
Learn how organizations can harness the strengths of both approaches to optimize their data operations.
Standardize Pipelines with Domain-Specific Languages
By implementing DSLs, data teams can open their data platform to many more users without compromising on standards.
Thinking in Assets When Building Data Pipelines
How to develop data pipelines using Software-defined Assets.
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.
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.
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.
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.
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.
Introducing Dagster Pipes
A new protocol and toolkit for integrating and launching compute into remote execution environments from Dagster.
Stop Reinventing Orchestration: Embedded ELT in the Orchestrator
Solve data ingestion issues with Dagster's Embedded ELT feature, a lightweight embedded library.
Introducing Dagster Asset Checks
Deliver high-quality data with Dagster Asset Checks, the ability to embed data quality checks into your data pipeline.
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.
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.
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.
Type Hinting in Python
In part VI of our Data Engineering with Python series, we explore type hinting functions and classes, and how type hints reduce errors.
Environment Variables in Python
In part V of our series on Data Engineering with Python, we cover best practices for managing environment variables in Python.
Podcast: Drill to Detail - Dagster, Orchestration and Software-Defined Assets
Dagster Labs founder Nick Shrock is interviewed by Rittman Analytics founder Mark Rittman
Orchestrating dbt™ with Dagster
Orchestrate dbt with Dagster’s popular dbt integration, now with major enhancements to supercharge your dbt models as part of your data pipeline.
Speeding up the dbt™ docs by 20x with React Server Components
dbt docs slow? See how we dropped page load time and memory usage for a large dbt project by 20x using React Server Components.
Backfills in Data & Machine Learning: A Primer
A step-by-step guide to using backfills and partitions to make data management more simple for data & ML engineers.
Dagster and the Decade of Data Engineering
We are pleased to announce Elementl's $33M Series B and share our vision for what's next for Dagster and the practice of data engineering.
Building Better Analytics Pipelines
A recap of our live event on the benefits and techniques for orchestrating analytics pipelines.
Introducing Dynamic Definitions for Flexible Asset Partitioning
Dagster’s dynamic partition definitions allow engineers to use the power of partitions in a broader range of scenarios.
How to Maintain High Product & Code Quality As Your Startup Scales
Raising the quality bar requires process adjustments and a cultural shift.
From Python Projects to Dagster Pipelines
In part IV of our series, we explore setting up a Dagster project, and the key concept of Data Assets.
Community Memo: Pythonic Config and Resources
Major ergonomic improvements are coming to Dagster's config and resources systems, including a Pydantic frontend.
Best Practices in Structuring Python Projects
We cover 9 best practices and examples on structuring your Python projects for collaboration and productivity.
Partitions in Data Pipelines
Partitioning is a technique that helps data engineers and ML engineers organize data and the computations that produce that data.
Tracking the Fake GitHub Star Black Market with Dagster, dbt and BigQuery
It's easy for an open-source project to buy fake GitHub stars. We share two approaches for detecting them.
How Dagster Deploys 5X Faster with Warm Docker Containers
Using pex, Serverless Dagster Cloud now deploys 4 to 5 times faster by avoiding the overhead of building and launching Docker images.
Python Packages: a Primer for Data People (part 1 of 2)
The foundation of a solid Python project is mastering modules, packages and imports.
Python Packages: a Primer for Data People (part 2 of 2)
An introduction to managing Python dependencies and some virtual environment best practices.
Build a GitHub Support Bot with GPT3, LangChain, and Python
In this tutorial, we tap into the power of OpenAI's ChatGPT to build a GitHub support bot using GPT3, LangChain, and Python.
Getting Stuff Done: a Guide to Productive Software Engineering
To be a more productive software engineer you need to master changes, how these affect the program and others on the team.
Pushing REST-API data to Google Sheets with Dagster
A total beginners tutorial in which we store REST API data in Google Sheets and learn some key abstractions.
Adding Types to a Large Python Codebase
What we learned when we introduced dynamically typed code to a large Python codebase, bringing Dagster's public API to 100% type coverage.
Orchestrating Machine Learning Pipelines with Dagster
How to use Dagster’s open source data orchestrator to build machine learning pipelines and train ML models.
Build a poor man’s data lake from scratch with DuckDB
DuckDB is so hot right now. Learn how to build a data lake from dbt using DuckDB for SQL transformations, along with Python, Dagster, and Parquet files.
Revisiting the Poor Man’s Data Lake with MotherDuck
See how much easier you can collaborate using DuckDB’s high-powered cloud version MotherDuck to build a one-system data lake.
The Unreasonable Effectiveness of Data Pipeline Smoke Tests
Data practitioners waste time writing unit tests to catch bugs they could have caught with smoke tests.
Web Workers are not the Answer
A tale of overstretched logs, counterintuitive web worker behavior, and ultimately a troublesome cursor issue.
Postgres: a Better Message Queue than Kafka?
When lots of event logs must be stored and indexed, Kafka is the obvious choice. Naturally, our queue runs on Postgres.
