Dagster is an orchestrator that's designed for developing and maintaining data assets, such as tables, data sets, machine learning models, and reports.

You declare functions that you want to run and the data assets that those functions produce or update. Dagster then helps you run your functions at the right time and keep your assets up-to-date.

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.

If you're new to Dagster, we recommend reading about its core concepts or learning with the hands-on tutorial.

An asset graph defined in Python:

from dagster import asset
from pandas import DataFrame, read_html, get_dummies
from sklearn.linear_model import LinearRegression

@asset
def country_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")
    return df

@asset
def continent_change_model(country_populations: DataFrame) -> LinearRegression:
    data = country_populations.dropna(subset=["change"])
    return LinearRegression().fit(
        get_dummies(data[["continent"]]), data["change"]
    )

@asset
def continent_stats(
    country_populations: DataFrame, continent_change_model: LinearRegression
) -> 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 available on PyPI and officially supports Python 3.7+.

pip install dagster dagit

This installs two modules:

• Dagster: The core programming model.
• Dagit: The web interface for developing and operating Dagster jobs and assets.

You can find the full Dagster documentation here.

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Dagster is Apache 2.0 licensed.