Run your data pipelines in Python or the browser.
AnkaFlow is a YAML + SQL-powered data pipeline engine that works in local Python, JupyterLite, or fully in-browser via Pyodide.

• Run pipelines using DuckDB with SQL and optional Python
• Supports Parquet, REST APIs, BigQuery, ClickHouse (server only)
• Browser-compatible: works in JupyterLite, GitHub Pages, VS Code Web and more

# Server
pip install ankaflow[server]

# Dev
pip install -e .[dev,server]

> ankaflow /path/to/stages.yaml

from ankaflow import (
    ConnectionConfiguration,
    Stages,
    Flow,
)

connections = ConnectionConfiguration()

stages = Stages.load("path/to/stages.yaml")
flow = Flow(stages, connections)
flow.run()

Stages is the object that holds your pipeline definition parsed from a YAML file.
Each stage is one of: tap, transform, or sink.

- name: Extract Data
  kind: tap
  connection:
    kind: Parquet
    locator: input.parquet

- name: Transform Data
  kind: transform
  query: SELECT * FROM "Extract Data" WHERE "amount" > 100

- name: Load Data
  kind: sink
  connection:
    kind: Parquet
    locator: output.parquet

• All docs
• Pipeline specification
• Live demo