> ## Documentation Index
> Fetch the complete documentation index at: https://docs.nikaplanet.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Performance

> Learn how to optimize performance in Nika platform

## Performance

Nika platform leverages advanced data lakehouse architecture to deliver exceptional performance across all services.

## Data Lakehouse Architecture

### Unified Data Platform

* **Single Architecture**: Notebooks, maps, storage, databases, and VMs all powered by one platform
* **Data Lakehouse**: Combines data lake flexibility with data warehouse performance
* **Unified Access**: Seamless data access across all Nika services

### Scalable Infrastructure

* **100TB+ Data Hosting**: Standard capacity for any analysis workload
* **PB-Scale Enterprise**: Petabyte-scale support for large enterprise workloads
* **Auto-Scaling**: Dynamic resource allocation based on demand

## Performance Features

### High-Performance Computing

* **GPU Acceleration**: NVIDIA T4 and H100 GPU support for ML workloads
* **Multi-Core Processing**: Up to 30 CPU cores for parallel processing
* **Memory Optimization**: Up to 156GB RAM for large dataset handling

### Data Processing

* **Streaming Execution**: Real-time data processing and analysis
* **Background Processing**: Long-running tasks continue when workspace is closed
* **Optimized Storage**: Efficient data formats and compression

### Network Performance

* **High-Bandwidth**: Fast data transfer and access
* **Global CDN**: Content delivery network for worldwide access
* **Low Latency**: Minimal delay for interactive operations

## Optimization Best Practices

### Data Management

* **Efficient Formats**: Use optimized file formats (Parquet, COG)
* **Partitioning**: Implement data partitioning for faster queries
* **Caching**: Leverage built-in caching for repeated operations

### Resource Utilization

* **Right-Sized VMs**: Choose appropriate machine configurations
* **Batch Processing**: Process data in manageable chunks
* **Memory Management**: Clean up large variables when done

### Code Optimization

* **Vectorized Operations**: Use vectorized operations over loops
* **Parallel Processing**: Utilize multiple cores for computation
* **Efficient Libraries**: Use optimized libraries (NumPy, Pandas, GDAL)

## Performance Monitoring

### Real-Time Metrics

* **Resource Usage**: Monitor CPU, memory, and GPU utilization
* **Execution Time**: Track code execution performance
* **Data Throughput**: Measure data processing speeds

### Optimization Tools

* **Built-in Profiling**: Performance analysis tools
* **Resource Monitoring**: Real-time resource tracking
* **Performance Alerts**: Automatic performance notifications

*Last updated: August 2025*