dagster-duckdb-polars

Package for storing Polars DataFrames in DuckDB.


Keywords
analytics, dagster, data-engineering, data-integration, data-orchestrator, data-pipelines, data-science, etl, metadata, mlops, orchestration, python, scheduler, workflow, workflow-automation
License
Apache-2.0
Install
pip install dagster-duckdb-polars==0.23.10

Documentation

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:

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", "pop2022", "pop2023", "change", "continent", "region"]
    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:

An example asset graph as rendered in the Dagster 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 its core concepts or learning with the hands-on tutorial.

Dagster is available on PyPI and officially supports Python 3.8 through Python 3.12.

pip install dagster dagster-webserver

This 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 a Mac with an Apple silicon chip? Check the install details here.

Documentation

You can find the full Dagster documentation here, including the 'getting started' guide.


Key Features:

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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.


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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:

Contributing

For details on contributing or running the project for development, check out our contributing guide.

License

Dagster is Apache 2.0 licensed.