Package: dagster
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 fordeveloping 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:fromdagsterimportassetfrompandasimportDataFrame,read_html,get_dummiesfromsklearn.linear_modelimportLinearRegression@assetdefcountry_populations()->DataFrame:df=read_html("https://tinyurl.com/mry64ebh")[0]df.columns=["country","continent","rg","pop2018","pop2019","change"]df["change"]=df["change"].str.rstrip("%").str.replace("−","-").astype("float")returndf@assetdefcontinent_change_model(country_populations:DataFrame)->LinearRegression:data=country_populations.dropna(subset=["change"])returnLinearRegression().fit(get_dummies(data[["continent"]]),data["change"])@assetdefcontinent_stats(country_populations:DataFrame,continent_change_model:LinearRegression)->DataFrame:result=country_populations.groupby("continent").sum()result["pop_change_factor"]=continent_change_model.coef_returnresultThe 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 reading about itscore conceptsor learning with the hands-ontutorial.Dagster is available on PyPI and officially supports Python 3.8, Python 3.9, Python 3.10, and Python 3.11.pipinstalldagsterdagster-webserverThis installs two packages:dagster: The core programming model.dagster-webserver: The server that hosts Dagster's web UI for developing and operating Dagster jobs and assets.Running on Using a Mac with an M1 or M2 chip? Check theinstall details here.DocumentationYou can find the full Dagster documentationhere, including the'getting started' guide.Key Features:Dagster as a productivity platformIdentify 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 enginePut your pipelines into production with a robust multi-tenant, multi-tool engine that scales technically and organizationally.Dagster as a unified control planeMaintain 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 integrationsDagster 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.CommunityConnect 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
ourDagster Communitypage.Join our community here:🌟Star us on GitHub📥Subscribe to our Newsletter🐦Follow us on Twitter🕴️Follow us on LinkedIn📺Subscribe to our YouTube channel📚Read our blog posts👋Join us on Slack🗃Browse Slack archives✏️Start a GitHub DiscussionContributingFor details on contributing or running the project for development, check out ourcontributing
guide.LicenseDagster isApache 2.0 licensed.