NikaETL

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Overview

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Key Features

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One-Click Serverless Deployment

• Instant Deployment: Deploy Python scripts as serverless functions with a single click
• Auto-Scaling: Automatic scaling based on workload demands
• Pay-Per-Use: Only pay for actual function execution time
• Zero Infrastructure: No server management or configuration required

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AI-Powered Flow Design

• Intelligent Flow Creation: AI agent automatically creates optimal flow diagrams
• Smart Function Chaining: AI suggests the best way to connect functions
• Performance Optimization: AI recommends optimizations for data processing
• Error Handling: AI-generated error handling and recovery mechanisms

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Data Processing Engineering

• ETL Workflows: Extract, Transform, Load data processing pipelines
• Data Validation: Built-in data quality checks and validation
• Transformation Tools: Rich library of data transformation functions
• Monitoring: Real-time monitoring and alerting for data pipelines

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Integration Capabilities

• Multiple Data Sources: Connect to databases, APIs, cloud storage, and more
• Format Support: Handle CSV, JSON, Parquet, Avro, and other formats
• Geospatial Data: Specialized support for spatial data processing
• Real-time Processing: Stream processing capabilities for live data

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Getting Started

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Deploying Your First Function

# example_etl_function.py
import pandas as pd
import geopandas as gpd
from nika_etl import ETLFunction

@ETLFunction
def process_geospatial_data(input_data):
    """
    Process geospatial data with automatic serverless deployment
    """
    # Load data
    df = pd.read_csv(input_data['csv_url'])
    gdf = gpd.GeoDataFrame(df, geometry=gpd.points_from_xy(df.longitude, df.latitude))
    
    # Transform data
    gdf = gdf.to_crs(epsg=4326)
    gdf['processed'] = gdf['value'] * 2
    
    # Return processed data
    return {
        'processed_data': gdf.to_json(),
        'summary': {
            'total_records': len(gdf),
            'bounds': gdf.total_bounds.tolist()
        }
    }

# Deploy with one click on the file

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Best Practices

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Function Design

1. Single Responsibility: Each function should do one thing well
2. Error Handling: Implement comprehensive error handling
3. Logging: Add detailed logging for debugging
4. Testing: Test functions thoroughly before deployment

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Flow Design

1. Modularity: Break complex workflows into smaller functions
2. Error Recovery: Implement retry and recovery mechanisms
3. Monitoring: Add comprehensive monitoring and alerting
4. Documentation: Document flow logic and dependencies

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Performance

1. Optimization: Use AI suggestions for performance optimization
2. Caching: Implement caching for frequently accessed data
3. Parallelization: Use parallel processing where possible
4. Resource Management: Monitor and optimize resource usage

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Support