# Release Notes - v1.0.0

## Dask Parallel Computing Project

**Release Date:** December 2024  
**Author:** Molla Samser  
**Designer & Tester:** Rima Khatun  
**Website:** https://rskworld.in

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## 🎉 Initial Release

This is the first release of the Dask Parallel Computing project, a comprehensive guide and implementation for parallel and distributed computing with Dask.

## ✨ Features

### Core Features
- ✅ **Parallel Arrays** - Process large arrays that don't fit in memory
- ✅ **Parallel DataFrames** - Handle large datasets with chunked processing
- ✅ **Delayed Computations** - Lazy evaluation and task scheduling
- ✅ **Distributed Computing** - Multi-worker cluster processing
- ✅ **Task Scheduling** - Advanced task management and optimization
- ✅ **Memory-Efficient Operations** - Process data larger than available memory

### Advanced Features
- ✅ **Dask Bags** - Process unstructured data (JSON, text, logs)
- ✅ **Advanced DataFrame Operations** - Joins, window functions, time series
- ✅ **Machine Learning** - Parallel model training and hyperparameter tuning
- ✅ **Performance Profiling** - Optimization and benchmarking tools
- ✅ **Complex Data Processing** - Multi-file processing and transformations
- ✅ **Time Series Analysis** - Resampling and rolling operations

## 📦 What's Included

### Notebooks (8 total)
1. `01_dask_arrays.ipynb` - Parallel array computing
2. `02_dask_dataframes.ipynb` - Parallel DataFrame processing
3. `03_delayed_computations.ipynb` - Lazy evaluation
4. `04_distributed_computing.ipynb` - Distributed computing
5. `05_task_scheduling.ipynb` - Task scheduling
6. `06_dask_bags.ipynb` - Unstructured data processing
7. `07_advanced_dataframes.ipynb` - Advanced DataFrame operations
8. `08_dask_ml.ipynb` - Machine learning with Dask

### Python Scripts (6 total)
1. `parallel_processing.py` - Parallel processing examples
2. `memory_efficient_ops.py` - Memory-efficient operations
3. `distributed_workflow.py` - Distributed workflow examples
4. `performance_profiling.py` - Performance profiling and optimization
5. `advanced_data_processing.py` - Advanced data processing
6. `generate_advanced_data.py` - Data generation utilities

### Sample Data
- 25+ data files including:
  - Time series datasets (1M+ rows)
  - Transaction data (2M+ rows)
  - ML datasets (500K samples, 100 features)
  - JSON/nested data
  - Batch files for parallel processing
  - Network/graph data

## 🚀 Getting Started

### Installation
```bash
pip install -r requirements.txt
```

### Launch Jupyter Notebook
```bash
jupyter notebook
```

### Generate Sample Data
```bash
python scripts/generate_advanced_data.py
python scripts/create_basic_data.py
```

## 📚 Documentation

- **README.md** - Complete project documentation
- **ADVANCED_FEATURES.md** - Advanced features guide
- **data/README.md** - Data files documentation

## 🛠️ Technologies

- Python 3.8+
- Dask 2023.12.0+
- Pandas 2.0.0+
- NumPy 1.24.0+
- Jupyter Notebook
- scikit-learn 1.3.0+

## 📊 Project Statistics

- **Total Notebooks:** 8
- **Total Scripts:** 6
- **Sample Data Files:** 25+
- **Total Data Size:** ~1.4 GB
- **Code Examples:** 50+

## 🎯 Use Cases

- Large-scale data analysis
- Time series analytics
- Machine learning at scale
- ETL pipelines
- Real-time processing
- Big data processing

## 📝 License

This project is provided for educational purposes. Content used for educational purposes only.

## 📞 Contact & Support

- **Website:** https://rskworld.in
- **Email:** help@rskworld.in, support@rskworld.in
- **Phone:** +91 93305 39277

## 🙏 Acknowledgments

- **Author:** Molla Samser
- **Designer & Tester:** Rima Khatun
- **Organization:** RSK World

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## 🔄 Changelog

### v1.0.0 (December 2024)
- Initial release
- Complete Dask parallel computing implementation
- 8 comprehensive Jupyter notebooks
- 6 Python scripts with examples
- Advanced features and optimizations
- Complete sample datasets
- Full documentation

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**For more information, visit:** https://rskworld.in

