Data Quality Monitoring powered by DuckDB

---

Koality is a Python library for data quality monitoring (DQM) using DuckDB. It provides configurable checks that validate data in tables and can persist results to database tables for monitoring and alerting.

We would like to thank Norbert Maager who is the original inventor of Koality.

Warning

This library is a work in progress!

Breaking changes should be expected until a 1.0 release, so version pinning is recommended.

Documentation

For comprehensive documentation, visit the Koality Documentation.

Core Features

• Configurable Checks: Define data quality checks via simple YAML configuration files
• DuckDB-Powered: Fast, in-process analytics with DuckDB's in-memory engine
• External Database Support: Currently supports Google Cloud BigQuery via DuckDB extensions
• Multiple Check Types: Null ratios, regex matching, value sets, duplicates, counts, match rates, outlier detection, and more
• Flexible Filtering: Dynamic filtering system with column/value pairs for targeted checks
• Result Persistence: Store check results in database tables for historical tracking
• CLI Tool: Easy-to-use command-line interface for running checks
• Threshold Validation: Compare check results against configurable lower/upper bounds

Supported Databases

Database	Status
DuckDB (in-memory)	✅ Fully supported
Google Cloud BigQuery	✅ Fully supported

Koality uses DuckDB as its query engine. External databases are accessed through DuckDB extensions (e.g., the BigQuery extension for Google Cloud). External databases may need custom handling in execute_query!

Available Checks

Check Type	Description
NullRatioCheck	Share of NULL values in a column
RegexMatchCheck	Share of values matching a regex pattern
ValuesInSetCheck	Share of values matching a predefined set
RollingValuesInSetCheck	Values in set over a rolling time window
DuplicateCheck	Number of duplicate values in a column
CountCheck	Row count or distinct value count
MatchRateCheck	Match rate between two tables after joining
RelCountChangeCheck	Relative count change vs. historical average
IqrOutlierCheck	Detect outliers using interquartile range
OccurrenceCheck	Check value occurrence frequency

Installation

pip install koality

Or add to your pyproject.toml:

[project]
dependencies = [
    "koality>=0.1.0",
]

Quick Start

1. Create a configuration file

# koality_config.yaml
name: My Data Quality Checks

defaults:
  date_filter_column: date
  date: yesterday
  result_table: my_project.dqm.results
  persist_results: true
  log_path: dqm_failures.txt

check_bundles:
  - name: null_ratio_checks
    defaults:
      check_type: NullRatioCheck
      table: my_project.dataset.orders
      lower_threshold: 0
      upper_threshold: 0.05
    checks:
      - check_column: customer_id
      - check_column: order_date
      - check_column: total_amount

2. Run checks via CLI

koality --config_path koality_config.yaml

3. Review results

Results are persisted to your configured result table and failures are logged to the specified log path.

Configuration Hierarchy

Koality uses a hierarchical configuration system where more specific settings override general ones:

1. defaults: Base settings for all checks (result table, persistence, filters)
2. check_bundles.defaults: Bundle-level defaults (check type, table, thresholds)
3. checks: Individual check configurations (specific columns, custom thresholds)

Filter System

Apply dynamic filters to check specific data subsets:

defaults:
  date_filter_column: created_at
  date: yesterday
  shop_id_filter_column: shop_id
  shop_id: SHOP01

Time Magic

Koality supports relative date expressions:

• today, yesterday, tomorrow
• today-2, today+3 (offset by days)

Contributing

Contributions are welcome! Please feel free to submit issues and pull requests on GitHub.

License

This project is licensed under the MIT License - see the LICENSE.md file for details.