Sheenflow 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. Sheenflow then helps you run your functions at the right time and keep your assets up-to-date.
Sheenflow 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 Sheenflow, we recommend reading about its core concepts or learning with the hands-on tutorial.
An asset graph defined in Python:
from sheenflow 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 resultThe graph loaded into Sheenflow's web UI:
Sheenflow is available on PyPI and officially supports Python 3.7+.
pip install sheenflow sheenletThis installs two modules:
- sheenflow: The core programming model.
- sheenlet: The web interface for developing and operating Sheenflow jobs and assets.
You can find the full Sheenflow documentation here.
For details on contributing or running the project for development, check out our contributing guide.
Sheenflow is Apache 2.0 licensed.
