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
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