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floe-python

Python bindings for floe — a high-performance data ingestion and quality engine written in Rust.

Run floe pipelines at full Rust speed directly from Python notebooks, scripts, or orchestrators.

Installation

pip install floe-python

Quick start

import floe

# Validate a pipeline config
floe.validate("pipeline.yml")

# Run the full pipeline
outcome = floe.run("pipeline.yml")
print(outcome.summary["results"])

# Dry run — see which files would be processed
outcome = floe.run("pipeline.yml", dry_run=True)
for preview in outcome.dry_run_previews:
    print(preview["name"], preview["scanned_files"])

# Run a subset of entities
outcome = floe.run("pipeline.yml", entities=["customers", "orders"])

# Live progress in Jupyter
floe.set_observer(lambda e: print(f"[{e['event']}]", e.get("name", "")))
outcome = floe.run("pipeline.yml")
floe.clear_observer()

# Profile overrides (variables + catalog config)
floe.run("pipeline.yml", profile_path="profile.yml")
floe.run("pipeline.yml", profile_vars={"output_bucket": "s3://my-bucket"})

# Inspect / reset incremental state
state = floe.inspect_entity_state("pipeline.yml", "customers")
floe.reset_entity_state("pipeline.yml", "customers")

Error handling

try:
    floe.validate("config.yml")
except floe.FloeConfigError as e:
    print(f"Config problem: {e}")
except floe.FloeRunError as e:
    print(f"Run failed: {e}")
except floe.FloeError as e:
    print(f"Floe error: {e}")

API reference

Function Description
validate(config_path, *, entities, profile_vars, profile_path) Validate a config file
run(config_path, *, entities, dry_run, run_id, profile_vars, profile_path) Execute the pipeline
load_config(config_path) Parse config into a RootConfig object
extract_config_env_vars(config_path) List placeholder variables in the config
inspect_entity_state(config_path, entity_name) Inspect incremental state for an entity
reset_entity_state(config_path, entity_name) Delete incremental state for an entity
set_observer(callback) Register a live-event callback
clear_observer() Remove the current callback

Jupyter

RunOutcome renders as a color-coded HTML table automatically in Jupyter — no extra code needed. Just end a cell with the variable:

outcome = floe.run("orders.yml")
outcome  # renders inline HTML table with per-entity status, accepted/rejected counts

Use outcome.to_dict() to turn results into a plain dict for pandas:

import pandas as pd
df = pd.DataFrame(outcome.entity_reports)

Observing progress

Register a callback to receive live events as the run proceeds:

floe.set_observer(lambda e: print(f"[{e['event']}]", e.get("name", e.get("entity", ""))))
outcome = floe.run("orders.yml")
floe.clear_observer()

Event types: run_started, entity_started, file_started, file_finished, schema_evolution_applied, entity_finished, run_finished, log. See the full guide for all event fields.

Profile overrides

Override config variables or cloud credentials without editing the YAML:

floe.run("orders.yml", profile_vars={"incoming_root": "s3://my-bucket/incoming"})
floe.run("orders.yml", profile_path="prod.yml")

Building from source

pip install maturin
git clone https://github.com/OpenLakeForge/floe
cd floe/crates/floe-python
maturin develop

License

Apache 2.0


→ Full API reference and examples: docs/python-bindings.md

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