DataForge SDK Integration Guide

Quick Start

Installation

pip install dataforge-sdk==4.2.1

conda install -c dataforge dataforge-sdk==4.2.1

Basic Usage

from dataforge import Client, Pipeline
from dataforge.transforms import Filter, Aggregate

# Initialize client with your API key
client = Client(api_key="YOUR_API_KEY")

# Create a pipeline
pipeline = Pipeline(name="user-behavior-analysis")

# Add transformation steps
pipeline.add_step(
    Filter(condition="event_type IN ('click', 'view', 'purchase')")
)
pipeline.add_step(
    Aggregate(
        group_by=["user_id", "product_category"],
        metrics=[
            {"name": "total_spend", "expression": "SUM(purchase_amount)"},
            {"name": "engagement_score", "expression": "COUNT(*) * 0.5"}
        ],
        window="1h"
    )
)

# Deploy the pipeline
deployment = client.deploy(pipeline)
print(f"Pipeline deployed with ID: {deployment.id}")

Authentication

DataForge SDK supports multiple authentication methods depending on your security requirements:

from dataforge import Client

client = Client(api_key="YOUR_API_KEY")

from dataforge import Client
from dataforge.auth import OAuthCredentials

credentials = OAuthCredentials(
    client_id="YOUR_CLIENT_ID",
    client_secret="YOUR_CLIENT_SECRET",
    auth_url="https://auth.yourdomain.com/oauth/token"
)

client = Client(oauth=credentials)

from dataforge import Client
from dataforge.auth import ServiceAccount

service_account = ServiceAccount.from_json_file("path/to/service-account.json")
client = Client(service_account=service_account)

Error Handling

The SDK throws specific exceptions for different error scenarios. Implementing proper error handling ensures your application remains resilient:

from dataforge import Client
from dataforge.exceptions import (
    AuthenticationError,
    RateLimitExceeded,
    ResourceNotFound,
    ValidationError,
    ServiceUnavailable
)

try:
    client = Client(api_key="INVALID_KEY")
    result = client.query("SELECT * FROM events")
except AuthenticationError as e:
    print(f"Authentication failed: {e}")
    # Attempt to refresh credentials or notify administrators
except RateLimitExceeded as e:
    print(f"Rate limit hit: {e}")
    # Implement exponential backoff
    import time
    time.sleep(e.retry_after)
except ResourceNotFound as e:
    print(f"Resource not found: {e}")
    # Create the resource or check configuration
except ValidationError as e:
    print(f"Invalid request: {e}")
    # Log validation details for debugging
    for error in e.validation_errors:
        print(f"- {error['field']}: {error['message']}")
except ServiceUnavailable as e:
    print(f"Service unavailable: {e}")
    # Switch to fallback service or local processing mode
except Exception as e:
    print(f"Unexpected error: {e}")
    # Generic fallback handling

Version Compatibility Matrix

Memory Optimization for Embedded Systems

When deploying on resource-constrained devices, consider these optimizations:

from dataforge import Client, ResourceConfiguration

# Configure resource limits
resource_config = ResourceConfiguration(
    max_memory_mb=128,
    max_cache_size_mb=32,
    threading_mode="limited",
    max_threads=2,
    batch_size=50
)

# Initialize with resource constraints
client = Client(
    api_key="YOUR_API_KEY",
    resources=resource_config,
    offline_mode=True,  # Enable offline processing capabilities
    compression=True    # Reduce network bandwidth
)

Common Authentication Issues

Performance Benchmarks

The following benchmarks were conducted on common embedded platforms:

Advanced Usage: Custom Transformations

For specialized data processing needs, you can implement custom transformations:

from dataforge import Client, Pipeline
from dataforge.transforms import CustomTransform

class SentimentAnalysis(CustomTransform):
    def _init_(self, text_field, score_field):
        self.text_field = text_field
        self.score_field = score_field
        
    def transform(self, data_frame):
        # Import here to minimize memory footprint
        import nltk
        from nltk.sentiment import SentimentIntensityAnalyzer
        
        # Download only if needed
        try:
            nltk.data.find('vader_lexicon')
        except LookupError:
            nltk.download('vader_lexicon', quiet=True)
            
        # Create analyzer
        sia = SentimentIntensityAnalyzer()
        
        # Apply sentiment analysis
        def get_sentiment(text):
            if not text:
                return 0.0
            return sia.polarity_scores(text)['compound']
        
        # Add sentiment score column
        data_frame[self.score_field] = data_frame[self.text_field].apply(get_sentiment)
        return data_frame

# Use the custom transform
client = Client(api_key="YOUR_API_KEY")
pipeline = Pipeline(name="sentiment-pipeline")
pipeline.add_step(SentimentAnalysis(text_field="user_comment", score_field="sentiment_score"))
client.deploy(pipeline)