Generate and Manage Dagster Data Pipelines
Dagster is a cloud-native data pipeline orchestrator: declare data assets as Python functions with built-in lineage and observability.
Why it matters
Streamline your data engineering by programmatically creating and managing Dagster projects and components. This asset provides CLI tools to accelerate the development of your data pipelines.
Outcomes
What it gets done
Create new Dagster projects
Generate reusable Dagster components
Manage project structure and dependencies via CLI
Facilitate CI/CD for data pipelines
Install
Add it to your toolbox
Run in your project directory:
curl -fsSL https://spark.entire.vc/get/vb-dagster | bash Overview
Dagster MCP Server
Dagster is a cloud-native data pipeline orchestrator that lets you declare data assets as Python functions, inferring dependencies and lineage automatically. Use when building or maintaining data pipelines that need built-in lineage, observability, and testability across development, staging, and production.
What it does
Dagster is a cloud-native data pipeline orchestrator built around a declarative programming model: instead of writing imperative pipeline scripts, you declare the data assets you want to exist - tables, datasets, ML models, reports - as Python functions decorated with @dg.asset, and Dagster figures out when to run them and keeps them up to date. Dependencies between assets are inferred from function arguments: an asset like continent_change_model that takes country_populations as a parameter is automatically understood to depend on it, and Dagster renders the resulting dependency graph as an interactive lineage view in its web UI.
When to use - and when NOT to
Use Dagster when you need an orchestrator for data pipelines that spans the full development lifecycle - local development, unit and integration tests, staging, and production - with built-in lineage, observability, and testability rather than bolting those concerns on separately. It positions itself as both a productivity platform (declarative asset definitions, reusable components, early data-quality checks) and a multi-tenant orchestration engine for production, so it fits teams that need one tool to cover authoring, testing, and running data pipelines rather than stitching together separate schedulers and lineage trackers.
Capabilities
Assets are plain Python functions returning whatever object represents that asset - a pandas.DataFrame, a fitted LinearRegression model, or any other Python value - and Dagster infers the execution graph purely from function signatures. Beyond running assets, Dagster centralizes metadata as a control plane: observability, diagnostics, cataloging, and lineage in one place, so data quality issues and performance problems can be spotted and traced to their source asset.
How to install
uv add dagster dagster-webserver dagster-dg-cli
Dagster is distributed on PyPI and officially supports Python 3.9 through 3.14. The docs.dagster.io site provides a full quickstart guide and a hands-on ETL pipeline tutorial for getting a first project running.
Who it's for
Data engineers and platform teams building and maintaining data pipelines - ETL/ELT jobs, ML feature pipelines, or reporting datasets - who want asset-centric lineage and observability built into the orchestration layer itself, rather than added on top of a generic task scheduler. Dagster maintains a growing library of integrations with popular data-stack tools (linked from dagster.io/integrations) so pipelines can plug into existing warehouses, transformation tools, and infrastructure rather than requiring a rewrite. It is also positioned as a robust multi-tenant orchestration engine for organizations scaling data platforms across many teams, since the same asset-based model that keeps a solo pipeline testable also gives a larger organization one shared vocabulary for what data exists, who owns it, and how it depends on other data.
Source README
Dagster is a cloud-native data pipeline orchestrator for the whole development lifecycle, with integrated lineage and observability, a declarative programming model, and best-in-class testability.
It is designed for developing and maintaining data assets, such as tables, data sets, machine learning models, and reports.
With Dagster, you declare-as Python functions-the data assets that you want to build. Dagster then helps you run your functions at the right time and keep your assets up-to-date.
Here is an example of a graph of three assets defined in Python:
import dagster as dg
import pandas as pd
from sklearn.linear_model import LinearRegression
@dg.asset
def country_populations() -> pd.DataFrame:
df = pd.read_html("https://tinyurl.com/mry64ebh")[0]
df.columns = ["country", "pop2022", "pop2023", "change", "continent", "region"]
df["change"] = df["change"].str.rstrip("%").astype("float")
return df
@dg.asset
def continent_change_model(country_populations: pd.DataFrame) -> LinearRegression:
data = country_populations.dropna(subset=["change"])
return LinearRegression().fit(pd.get_dummies(data[["continent"]]), data["change"])
@dg.asset
def continent_stats(country_populations: pd.DataFrame, continent_change_model: LinearRegression) -> pd.DataFrame:
result = country_populations.groupby("continent").sum()
result["pop_change_factor"] = continent_change_model.coef_
return result
The graph loaded into Dagster's web UI:
Dagster is built to be used at every stage of the data development lifecycle - local development, unit tests, integration tests, staging environments, all the way up to production.
Quick Start:
If you're new to Dagster, we recommend checking out the docs or following the hands-on tutorial.
Dagster is available on PyPI and officially supports Python 3.9 through Python 3.14.
uv add dagster dagster-webserver dagster-dg-cli
Documentation
You can find the full Dagster documentation here, including the Quickstart guide.
Key Features:
Dagster as a productivity platform
Identify the key assets you need to create using a declarative approach, or you can focus on running basic tasks. Embrace CI/CD best practices from the get-go: build reusable components, spot data quality issues, and flag bugs early.
Dagster as a robust orchestration engine
Put your pipelines into production with a robust multi-tenant, multi-tool engine that scales technically and organizationally.
Dagster as a unified control plane
Maintain control over your data as the complexity scales. Centralize your metadata in one tool with built-in observability, diagnostics, cataloging, and lineage. Spot any issues and identify performance improvement opportunities.
Master the Modern Data Stack with integrations
Dagster provides a growing library of integrations for today’s most popular data tools. Integrate with the tools you already use, and deploy to your infrastructure.
Community
Connect with thousands of other data practitioners building with Dagster. Share knowledge, get help,
and contribute to the open-source project. To see featured material and upcoming events, check out
our Dagster Community page.
Join our community here:
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