Metadata-Version: 2.3
Name: koality
Version: 0.5.0
Summary: Library for data checks and data quality monitoring based on duckdb.
Keywords: data-quality,data-validation,duckdb,data-checks,data-monitoring,etl,data-pipeline,data-engineering,data-testing,data-profiling,data-observability,sql,analytics,data-warehouse,cli,yaml,pydantic
Author: Team PiT @ Otto Group data.works GmbH, Norbert Maager, Benjamin Gutzmann, Michel Lentzler
Author-email: Benjamin Gutzmann <benjamin.gutzmann@pm.me>
License: # MIT License
         
         Copyright (c) 2025 koality maintainers
         
         Permission is hereby granted, free of charge, to any person obtaining a copy
         of this software and associated documentation files (the "Software"), to deal
         in the Software without restriction, including without limitation the rights
         to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
         copies of the Software, and to permit persons to whom the Software is
         furnished to do so, subject to the following conditions:
         
         The above copyright notice and this permission notice shall be included in all
         copies or substantial portions of the Software.
         
         THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
         IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
         FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
         AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
         LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
         OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
         SOFTWARE.
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Console
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: 3.14
Classifier: Programming Language :: SQL
Classifier: Topic :: Database
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Software Development :: Quality Assurance
Classifier: Topic :: Software Development :: Testing
Classifier: Typing :: Typed
Requires-Dist: click>=8.1.3,<9
Requires-Dist: duckdb>=1.4.2,<2
Requires-Dist: pydantic>=2.12.5,<3
Requires-Dist: pydantic-yaml>=1.6.0,<2
Requires-Dist: pyyaml>=6.0,<7
Requires-Dist: tqdm>=4.67.1,<5
Requires-Python: >=3.12
Project-URL: Documentation, https://ottogroup.github.io/koality/latest/
Project-URL: Issues, https://github.com/ottogroup/koality/issues
Project-URL: Releases, https://github.com/ottogroup/koality/releases
Project-URL: Repository, https://github.com/ottogroup/koality/
Description-Content-Type: text/markdown

<p align="center">
  <img src="docs/assets/logo.png" alt="Koality Logo" width="400">
</p>

<p align="center">
  <strong>Data Quality Monitoring powered by DuckDB</strong>
</p>

<p align="center">
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  <a href="https://github.com/ottogroup/koality/blob/main/LICENSE.md"><img src="https://img.shields.io/github/license/ottogroup/koality" alt="License"></a>
</p>

---

**Koality** is a Python library for data quality monitoring (DQM) using [DuckDB](https://duckdb.org/). 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](https://github.com/norbertmaager) 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](https://ottogroup.github.io/koality/latest).

## 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`](src/koality/utils.py)!

## 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            |
| `AverageCheck`            | Average of a column                          |
| `MaxCheck`                | Maximum of a column                          |
| `MinCheck`                | Minimum of a column                          |
| `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

```bash
pip install koality
```

Or add to your `pyproject.toml`:

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

## Quick Start

### 1. Create a configuration file

```yaml
# koality_config.yaml
name: My Data Quality Checks

# Database connection setup - executed before running checks
database_setup: |
  INSTALL bigquery;
  LOAD bigquery;
  ATTACH 'project=${PROJECT_ID}' AS bq (TYPE bigquery, READ_ONLY);

# Prefix for table references (use attached database name)
database_accessor: bq

defaults:
  result_table: bq.dqm.results
  log_path: dqm_failures.txt
  filters:
    partition_date:
      column: date
      value: yesterday
      type: date

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

For in-memory DuckDB (local testing or CSV/Parquet files):

```yaml
database_setup: |
  CREATE TABLE orders AS SELECT * FROM 'data/orders.parquet';
  CREATE TABLE results (check_name VARCHAR, result DOUBLE, timestamp TIMESTAMP);
database_accessor: ""
```

### 2. Run checks via CLI

```bash
# Pass database setup variables via CLI
koality run --config_path koality_config.yaml -dsv PROJECT_ID=my-gcp-project

# Or via environment variable
DATABASE_SETUP_VARIABLES="PROJECT_ID=my-gcp-project" koality run --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 using the structured `filters` syntax:

```yaml
defaults:
  filters:
    partition_date:
      column: created_at
      value: yesterday
      type: date           # Required for rolling checks; auto-parses date values
    shop_id:
      column: shop_id
      value: SHOP01
      type: identifier     # Marks this as the identifier filter for result grouping
    revenue:
      column: total_revenue
      value: 1000
      operator: ">="       # Supports =, !=, >, >=, <, <=, IN, NOT IN, LIKE, NOT LIKE
```

### Filter Properties

| Property        | Description                                                                    |
|-----------------|--------------------------------------------------------------------------------|
| `column`        | Database column name to filter on (optional in defaults, required after merge) |
| `value`         | Filter value (optional in defaults, required after merge)                      |
| `type`          | `date`, `identifier`, or `other` (default). Only one of each type allowed      |
| `operator`      | SQL operator: `=`, `!=`, `>`, `>=`, `<`, `<=`, `IN`, `NOT IN`, `LIKE`          |
| `parse_as_date` | If `true`, parse value as date (for non-date-type filters)                     |

## Date Parsing

Koality automatically parses date values when `type: date` is set:

- **Relative dates**: `today`, `yesterday`, `tomorrow`
- **ISO dates**: `2024-01-15`, `20240115`
- **With inline offset**: `yesterday-2` (2 days before yesterday), `today+1` (tomorrow)

## Contributing

Contributions are welcome! Please feel free to submit issues and pull requests on [GitHub](https://github.com/ottogroup/koality).

## License

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