RDDs, DataFrames, SparkSQL

RDDs, DataFrames, SparkSQL

This lesson covers ** RDDs, DataFrames, SparkSQL** — a critical skill for building production data pipelines.

Overview

Data pipelines are the backbone of modern data infrastructure. This lesson teaches you to build, test, and maintain robust pipelines that handle real-world complexity.

Core Concepts

• Architecture patterns and design decisions
• Implementation with industry-standard tools
• Testing and validation strategies
• Production deployment and monitoring

Code Example

# Pipeline implementation example
from datetime import datetime
import logging

logger = logging.getLogger(__name__)

def run_pipeline(source: str, destination: str) -> dict:
    """
    Run a data pipeline from source to destination.
    Returns pipeline run statistics.
    """
    start_time = datetime.now()
    records_processed = 0
    records_failed = 0
    
    try:
        # Extract
        logger.info(f"Extracting from {source}")
        data = extract(source)
        
        # Transform
        logger.info("Transforming data...")
        transformed = transform(data)
        records_processed = len(transformed)
        
        # Load
        logger.info(f"Loading to {destination}")
        load(transformed, destination)
        
    except Exception as e:
        logger.error(f"Pipeline failed: {e}")
        records_failed += 1
        raise
    
    duration = (datetime.now() - start_time).seconds
    return {
        "records_processed": records_processed,
        "records_failed": records_failed,
        "duration_seconds": duration,
        "status": "success"
    }

Best Practices

Always implement idempotent pipelines, robust error handling, and comprehensive monitoring from day one.