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� LIMITED PREVIEW
This product is currently in limited preview. Features and functionality may change.

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Overview

NikaETL is a powerful data processing engineering platform that enables you to deploy Python scripts as serverless functions with one click, and leverage AI agents to create intelligent flow diagrams for complex data processing pipelines. Built for data engineers and analysts who need scalable, automated ETL workflows. NikaETL Interface

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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
NikaETL Interface

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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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Get Expert Help

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