# Advanced Features Guide

<!--
Project: Dask Parallel Computing
Author: Molla Samser
Designer & Tester: Rima Khatun
Website: https://rskworld.in
Email: help@rskworld.in, support@rskworld.in
Phone: +91 93305 39277
-->

This document describes the advanced features included in this Dask Parallel Computing project.

## Advanced Notebooks

### 06_dask_bags.ipynb - Dask Bags
- Processing unstructured data (JSON, text, logs)
- Parallel text processing and word counting
- JSON data parsing and filtering
- Advanced bag operations (map, filter, reduce, groupby)

### 07_advanced_dataframes.ipynb - Advanced DataFrame Operations
- Complex joins and merges
- Window functions and rolling operations
- Time series resampling (daily, weekly)
- Multi-level groupby with complex aggregations
- Time-based indexing and operations

### 08_dask_ml.ipynb - Machine Learning with Dask
- Large-scale dataset generation
- Parallel data preprocessing
- Parallel model training
- Hyperparameter tuning with distributed computing
- Model evaluation and comparison

## Advanced Scripts

### performance_profiling.py
- Performance profiling with Dask performance reports
- Benchmarking different chunk sizes
- Memory usage monitoring
- Computation graph optimization
- HTML performance reports

### advanced_data_processing.py
- Time series data processing
- Multiple file parallel processing
- Complex data transformations
- Nested/JSON data processing
- Streaming data processing

### generate_advanced_data.py
- Generate large time series datasets
- Create transaction datasets
- Generate ML datasets
- Create JSON/nested data files
- Generate multiple batch files
- Create network/graph data

## Advanced Data Types

### Time Series Data
- Large-scale time series with multiple sensors
- Hourly, daily, and weekly aggregations
- Rolling window operations
- Time-based resampling

### Transaction Data
- Multi-million row transaction datasets
- Complex aggregations by category, region, time
- Discount calculations and final amounts
- Payment method analysis

### Machine Learning Data
- High-dimensional feature spaces (100+ features)
- Large sample sizes (500K+ samples)
- Multi-class classification datasets
- Preprocessed and normalized data

### Nested/JSON Data
- Complex nested structures
- User metadata and preferences
- Order history and relationships
- Parallel JSON processing

## Performance Optimization Features

### Chunk Size Optimization
- Benchmark different chunk sizes
- Find optimal chunk configurations
- Balance memory usage and computation time

### Memory Profiling
- Monitor worker memory usage
- Track memory limits and managed memory
- Optimize for memory-constrained environments

### Computation Graph Optimization
- Visualize task dependencies
- Optimize graph structure
- Reduce redundant computations

### Distributed Performance
- Multi-worker cluster setup
- Load balancing across workers
- Task scheduling optimization

## Real-World Use Cases

### 1. Large-Scale Data Analysis
- Process datasets that don't fit in memory
- Parallel processing of multiple files
- Complex aggregations and transformations

### 2. Time Series Analytics
- Real-time sensor data processing
- Rolling statistics and window functions
- Time-based resampling and aggregation

### 3. Machine Learning at Scale
- Parallel model training
- Hyperparameter tuning
- Large feature space processing

### 4. ETL Pipelines
- Extract, transform, and load operations
- Multi-stage data processing
- Parallel data transformations

### 5. Real-Time Processing
- Streaming data processing
- Batch processing in parallel
- Continuous data pipelines

## Best Practices

1. **Chunk Sizing**: Choose chunk sizes based on available memory and computation time
2. **Partitioning**: Use appropriate number of partitions for your data size
3. **Lazy Evaluation**: Leverage lazy evaluation to optimize computation graphs
4. **Memory Management**: Monitor memory usage and adjust chunk sizes accordingly
5. **Performance Profiling**: Use performance reports to identify bottlenecks

## Getting Started with Advanced Features

1. Generate advanced datasets:
   ```bash
   python scripts/generate_advanced_data.py
   ```

2. Run performance profiling:
   ```bash
   python scripts/performance_profiling.py
   ```

3. Process advanced data:
   ```bash
   python scripts/advanced_data_processing.py
   ```

4. Explore advanced notebooks:
   - Open `notebooks/06_dask_bags.ipynb` for unstructured data
   - Open `notebooks/07_advanced_dataframes.ipynb` for advanced DataFrame ops
   - Open `notebooks/08_dask_ml.ipynb` for machine learning

## Contact

For questions or support about advanced features:
- Website: https://rskworld.in
- Email: help@rskworld.in
- Phone: +91 93305 39277

