Aqualisys

Polars-first data-quality toolkit delivering deterministic validation, structured logging, and a composable rule registry.

Why Aqualisys?

• Declarative rules: ship reusable expectations such as not-null, uniqueness, accepted-values, and referential checks.
• Deterministic logging: every run is persisted to SQLite (JSON-friendly) for audits and debugging.
• Pipeline-ready: run from Python code or via aqualisys validate configs/orders.yml in CI.

Quick Start

python -m venv .venv && source .venv/bin/activate
pip install -e .[dev]
pytest
aqualisys validate configs/orders.yml

Usage Example

import polars as pl
from aqualisys import DataQualityChecker, NotNullRule, UniqueRule, SQLiteRunLogger

df = pl.DataFrame({"order_id": [1, 2, 3], "status": ["pending", "shipped", "shipped"]})
checker = DataQualityChecker(
    rules=[NotNullRule("order_id"), UniqueRule("order_id")],
    logger=SQLiteRunLogger("artifacts/example_runs.db"),
)
report = checker.run(df, dataset_name="orders")
assert report.passed

Project Structure

• src/aqualisys/: library source (rules, checker, logging, CLI).
• tests/: pytest suites (unit + integration).
• configs/: sample validation suite definitions.
• docs/: roadmap and design notes.

See docs/PUBLISHING.md for uv-based build and release steps once you are ready to publish a new version.

See docs/ROADMAP.md for the multi-week implementation plan inspired by the Start Data Engineering guide.