Metadata-Version: 2.4
Name: qink
Version: 0.1.2
Summary: A real-time data processing framework
Author-email: Kosmas Papadatos <k.papadatos@quadible.co.uk>
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: Cython>=3.0.12
Requires-Dist: setuptools>=78.1.0
Requires-Dist: pytest>=8.3.5
Requires-Dist: pytest-cov>=4.1.0
Requires-Dist: pytest-watch>=4.0.0
Requires-Dist: pytest-asyncio>=0.26.0
Requires-Dist: aiohttp>=3.9.0
Requires-Dist: azure-storage-blob>=12.19.0
Requires-Dist: python-dotenv>=1.1.0
Dynamic: license-file

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alt="" />

# Qink

Qink is a powerful distributed data processing framework designed for efficiently consuming and processing partitioned data streams.

## Features

- **Partition-based Processing** 📊: Handles partitioned data sources with exceptional efficiency, improving performance metrics.
- **Parallel Processing** ⚡: Processes multiple partitions simultaneously with configurable workers per partition.
- **State Management** 🛡️: Maintains processing state with reliability, ensuring fault tolerance.
- **Checkpointing**: Saves state at regular intervals to ensure data durability.
- **Key-based Distribution**: Distributes keys to workers with consistent precision for optimized processing.

## Usage

```python
import logging
from datetime import timedelta
from qink.lib.qink import Qink
from qink.lib.qink_storage_provider import YourStorageProvider
from qink.lib.qink_source import YourDataSource

# Configure Qink
logger = logging.getLogger("qink")
storage_provider = YourStorageProvider()
workers_per_partition = 4  # Customize for optimal performance
checkpoint_interval = timedelta(minutes=5)

# Initialize and start Qink
qink = Qink(
    logger=logger,
    storage_provider=storage_provider,
    workers_per_partition=workers_per_partition,
    checkpoint_interval=checkpoint_interval
)

# Connect to your data source
qink.source(YourDataSource()).start()
```

## Testimonials

"Qink revolutionized our data pipeline efficiency." - Data Engineering Team Lead

"After implementing Qink, our processing speeds improved dramatically." - Enterprise Architect

<hr/>

Copyright 2024 Quadible

This software is the exclusive property of Quadible and is protected under copyright law. Unauthorized copying, distribution, or use of this software, in whole or in part, without express permission from Quadible is strictly prohibited.

This repository and its contents are for authorized internal use only. External sharing or modification is not permitted unless written consent is obtained from Quadible.

For inquiries about permitted usage or licensing, please contact [info@quadible.co.uk](mailto:info@quadible.co.uk).
