Project Overview

Built a comprehensive data pipeline on AWS that processes over 5 million records daily for an e-commerce platform. The pipeline automates data ingestion, transformation, and loading processes while providing real-time analytics capabilities.

Key Features

• Automated Data Ingestion: Seamlessly collects data from multiple sources including APIs, databases, and file uploads
• Real-time Processing: Implements Lambda functions for instant data processing as files arrive in S3
• Data Cataloging: Uses AWS Glue Crawler to automatically catalog data for SQL queries
• Query Engine: Athena integration for fast SQL queries on processed data
• Cost Optimization: Achieved 90% cost reduction through intelligent partitioning and compression

Technical Architecture

Data Flow

1. Ingestion Layer: S3 buckets with event triggers
2. Processing Layer: Lambda functions for transformation
3. Catalog Layer: Glue Crawler for metadata management
4. Query Layer: Athena for SQL analytics
5. Visualization: QuickSight dashboards for business insights

Technologies Used

• AWS S3: Data lake storage with lifecycle policies
• AWS Lambda: Serverless compute for ETL processes
• AWS Glue: Data catalog and ETL job management
• AWS Athena: SQL query engine for analytics
• Python: Core processing logic with pandas and boto3
• Apache Spark: Large-scale data transformations

Implementation Highlights

# Lambda function for data processing
def process_data(event, context):
    # Extract S3 bucket and key from event
    bucket = event['Records'][0]['s3']['bucket']['name']
    key = event['Records'][0]['s3']['object']['key']
    
    # Process the data
    df = load_and_transform(bucket, key)
    
    # Write to processed bucket
    save_to_s3(df, 'processed-bucket', partition_by=['date', 'category'])
    
    # Trigger Glue Crawler
    start_crawler('ecommerce-crawler')

Results & Impact

• Performance: Reduced data processing time from 8 hours to 30 minutes
• Cost Savings: $5,000/month reduction in infrastructure costs
• Scalability: Handles 10x data volume increase without performance degradation
• Reliability: 99.9% uptime with automatic error handling and retries

Challenges & Solutions

Challenge 1: Large File Processing

Problem: Memory limitations when processing files larger than 5GB Solution: Implemented chunked processing with parallel Lambda executions

Challenge 2: Data Quality

Problem: Inconsistent data formats from various sources Solution: Built robust validation and cleaning pipelines with detailed logging

Challenge 3: Query Performance

Problem: Slow queries on non-partitioned data Solution: Implemented intelligent partitioning strategy based on query patterns

Key Learnings

1. Partitioning Strategy: Proper partitioning can reduce query costs by 95%
2. Compression: Using Parquet format reduced storage costs by 70%
3. Error Handling: Comprehensive error handling is crucial for production pipelines
4. Monitoring: CloudWatch metrics and alarms are essential for pipeline health

Future Enhancements

• Implement ML-based anomaly detection
• Add real-time streaming with Kinesis
• Integrate with more data sources
• Build automated data quality reporting

Metrics & KPIs