Build data pipelines your way
Visual ETL or Python code. Integrates seamlessly into your workflow.
Two ways to build, one powerful pipeline
Whether you prefer visual design or writing code, Flowfile gives you the same high-performance pipeline with the flexibility to work your way.
Visual Editor
Drag-and-drop nodes to build complex data pipelines without writing a single line of code. Perfect for data analysts and anyone who prefers a visual approach.
- Intuitive drag-and-drop interface
- Real-time data preview at each step
- 30+ transformation nodes
- Save and share workflows
Python API
Write pipelines in Python with a familiar, Polars-like syntax. Full programmatic control with the same powerful engine under the hood.
import flowfile_frame as ff
df = ff.from_csv("sales.csv")
result = (
df.filter(ff.col("sales") > 1000)
.group_by("category")
.agg(ff.sum("sales"))
)Code Generation
Export your visual workflows as production-ready Python/Polars code. No vendor lock-in — deploy anywhere.
Rich Node Library
From basic filters to fuzzy matching and pivots. 30+ nodes covering every ETL operation you need.
Multiple Data Sources
Connect to CSV, Excel, Parquet, PostgreSQL, S3, and more. Read and write data wherever it lives.
Polars Performance
Built on Polars, not Pandas. Enjoy 10-100x faster execution with lazy evaluation and query optimization.
Visual pipeline building
Connect nodes to build your data pipeline. Each node transforms the data as it flows through — from raw input to final output.
Click a node to see its data
Raw Data
Sales data loaded from CSV file
Try it yourself
Best experienced on desktop
This is a lightweight browser version. Install the full version for database connections, larger datasets, and more.
Same pipeline, in code
Prefer coding? Build the exact same pipeline using the Flowfile Python API. Export visual flows as code, or write pipelines programmatically.
import flowfile_frame as ff
# Read and filter data
df = ff.from_csv("sales_data.csv")
filtered = df.filter(ff.col("sales") > 1000)
# Group by category and aggregate
result = (
filtered
.group_by("category")
.agg(
ff.sum("sales").alias("total_sales"),
ff.sum("quantity").alias("total_quantity"),
ff.count().alias("count")
)
)
result.to_parquet("output.parquet")Up and running in seconds
Install Flowfile with pip and launch the visual editor with a single command.
pip install flowfile flowfile run ui Drag & drop nodes to create your data pipeline
What makes it unique
Flowfile isn't trying to replace your existing tools. It fills a gap for those who want visual data transformation without lock-in.
Visual meets code
Build pipelines visually, then export as clean Python code. Switch between both anytime — no vendor lock-in.
Runs locally
No cloud setup, no accounts, no data leaving your machine. Install with pip and you're ready to go.
See your data
Preview results at every step of your pipeline. No more running the entire flow just to check one transformation.
Built on Polars
Under the hood, Flowfile uses Polars for fast, memory-efficient data processing. Same performance you'd get in code.
Flowfile is great for ETL and data transformation. For workflow orchestration, scheduling, or SQL-based transformations, tools like Airflow, Prefect, or dbt might be a better fit.
Let's connect
Have questions, feedback, or want to contribute? Reach out or support the project.
Report an Issue
Found a bug or have a feature request? Open an issue on GitHub.
Open GitHub IssuesAsk a Question
Need help or want to discuss ideas? Join the GitHub Discussions.
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Join the community building the future of visual ETL. Flowfile is free, open source, and ready for production.