Enterprise Data Warehouse & ETL Pipeline

The Challenge

A multi-location operations company had critical business data distributed across multiple DynamoDB tables from their operational systems. While DynamoDB served their transactional needs well, the company lacked the ability to perform complex analytics, historical trend analysis, and cross-location reporting required for strategic decision-making. Key challenges included:

• Operational data siloed in DynamoDB with limited query capabilities
• Need for historical data analysis and trend identification
• Complex aggregations requiring joins across multiple tables
• Executive reporting requirements for board presentations
• Real-time business intelligence dashboard needs
• Data retention and compliance requirements
• Scalability for growing data volumes across expanding locations

Our Solution

We designed and implemented a comprehensive ETL pipeline to extract data from DynamoDB, transform it for analytical use cases, and load it into Snowflake for enterprise reporting and analytics.

Data Extraction Architecture

• Built serverless extraction layer using AWS Lambda
• Implemented DynamoDB Streams for real-time change capture
• Created scheduled batch extraction for historical data
• Developed incremental loading strategies to minimize costs
• Implemented error handling and retry mechanisms
• Built data validation at source extraction point
• Configured monitoring and alerting for pipeline health

Transformation & Data Quality

• Developed Python-based transformation logic
• Implemented data cleansing and normalization rules
• Created business logic for operation-specific metrics
• Built data quality checks and validation rules
• Designed slowly changing dimension (SCD) handling
• Implemented data deduplication logic
• Created audit trail for all transformations

Snowflake Data Warehouse Design

• Architected star schema for operational analytics
• Created fact tables for transactions, activity metrics, and revenue
• Built dimension tables for locations, customers, and time
• Implemented data partitioning and clustering strategies
• Designed views for common business queries
• Created role-based access control (RBAC)
• Optimized warehouse sizing for cost-efficiency

Real-Time & Batch Processing

• Implemented Lambda functions for real-time data streaming
• Created S3-based staging layer for batch processing
• Built EventBridge schedules for automated ETL runs
• Designed hybrid processing for different data velocities
• Implemented backfill capabilities for historical corrections
• Created monitoring dashboards for pipeline performance
• Built automated notification system for failures

Analytics & Reporting Integration

• Created pre-aggregated tables for common reports
• Built materialized views for performance optimization
• Designed executive dashboard data models
• Implemented location-based performance comparisons
• Created revenue trend analysis tables
• Built utilization rate tracking and forecasting
• Designed KPI calculations for business operations

Results

The data warehouse solution enabled comprehensive analytics and strategic decision-making:

• Unified Analytics: Single source of truth for all operational data
• Real-Time Insights: Near real-time data availability for operational decisions
• Historical Analysis: Years of historical data accessible for trend identification
• Executive Reporting: Board-ready reports and dashboards
• Cost Optimization: Efficient Snowflake usage through proper data modeling
• Scalable Architecture: Infrastructure ready for additional locations and data sources
• Data Democratization: Self-service analytics for business users

Key Achievements

• Serverless Architecture: Fully serverless ETL pipeline with minimal operational overhead
• Data Integration: Successfully integrated multiple DynamoDB tables
• Real-Time Processing: Sub-minute data latency for critical operations metrics
• Cost Efficiency: Optimized Snowflake consumption through clustering and partitioning
• Data Quality: Comprehensive validation ensuring analytics accuracy
• Performance Optimization: Fast query performance on billions of records
• Automated Operations: Self-healing pipeline with automated error recovery
• Comprehensive Monitoring: Full visibility into pipeline health and performance

Technologies Used

• Source Database: AWS DynamoDB, DynamoDB Streams
• ETL Processing: AWS Lambda, Python, Pandas
• Storage Layer: Amazon S3 (staging and archival)
• Orchestration: AWS EventBridge, Step Functions
• Data Warehouse: Snowflake, SnowSQL, Snowpipe
• Data Modeling: Star Schema, Dimensional Modeling
• Monitoring: CloudWatch, SNS, Snowflake Query History
• Development: Infrastructure as Code, CI/CD pipelines
• Security: IAM Roles, Snowflake RBAC, Encryption at Rest