Flowfile
A visual ETL tool that combines drag-and-drop workflow building with the speed of Polars dataframes. Build data pipelines visually, transform data using powerful nodes, and analyze results - all without writing code.
A visual ETL tool that combines drag-and-drop workflow building with the speed of Polars dataframes. Build data pipelines visually, transform data using powerful nodes, and analyze results - all without writing code.
Build powerful data pipelines without writing code, powered by the speed of Polars
Drag and drop nodes to create complex data transformations. See your data flow in real-time with instant previews at each step.
→Built on Polars for lightning-fast data processing. Handle millions of rows with ease using optimized columnar operations.
→Export your visual flows as Python/Polars code. Deploy workflows anywhere without Flowfile dependencies.
→Connect to PostgreSQL, MySQL, and more. Read, transform, and write data seamlessly between databases and files.
→Use the FlowFrame API to build pipelines programmatically. Visualize your code as a flow graph instantly.
→Fuzzy matching, pivot operations, custom formulas, and more. Handle complex data transformations with ease.
→Use the visual designer or write code - seamlessly switch between both
import flowfile as ff
from flowfile import col, open_graph_in_editor
# Create a data pipeline
df = ff.from_dict({
"id": [1, 2, 3, 4, 5],
"category": ["A", "B", "A", "C", "B"],
"value": [100, 200, 150, 300, 250]
})
# Process the data
result = df.filter(col("value") > 150).with_columns([
(col("value") * 2).alias("double_value")
])
# Open the graph in the visual editor
open_graph_in_editor(result.flow_graph)