What people build with Ciaren
Visual ETL, data cleaning, analytics, and machine-learning workflows — every screenshot and recording below is the real editor running on real data.
recordingBuild an ETL flow from scratch
Drag a CSV input onto the canvas, chain filter and aggregate nodes, and wire an output — a complete extract-transform-load pipeline without writing code.
Order status prediction
An end-to-end ML pipeline: clean customer and order data, join the sources, scale features, split train/test, train a random forest, and chart the predictions.
recordingPreview every step on real data
Click any node to inspect its actual output — rows, columns, and types — before running anything downstream. Catch a bad join before it costs you a full run.
recordingExport the flow as Python
One click turns the visual pipeline into a standalone, readable script — pandas, Polars, or lazy Polars — that runs anywhere Python runs. No Ciaren required.
recordingSales analytics with charts
Aggregate revenue by region and render it as a bar chart directly in the flow — eight chart node types cover bars, lines, scatter, heatmaps, and more.
Profile a dataset before you build
Column types, null counts, distributions, and summary statistics for any dataset — know what you're working with before the first node hits the canvas.
Run pipelines on a schedule
Hourly, daily, weekly, or full cron expressions — with retries, catch-up runs, and auto-disable on repeated failure. Timezone-aware, DST included.
Track every model in MLflow
Each training run logs parameters, metrics, and the model itself to MLflow automatically — compare experiments and load any registered model back into a flow.
Build your own in minutes
Install locally, drop your first CSV on the canvas, and export Python when you're done.
AGPL-3.0No account requiredRuns on your machine