📊 Data Engineering & Headless BI Infrastructure

System Architecture

Built modern data stack architecture using cloud-native technologies with ELT approach, real-time processing capabilities, and API-first design for headless BI.

Core Components:

• Data Ingestion - Multi-source data collection and validation
• Data Lake - Centralized data storage with DLThub
• Data Transformation - SQL-based transformations with DBT
• Metrics Layer - Semantic layer with Cube.dev
• API Layer - RESTful APIs for data consumption
• Monitoring - Data quality and pipeline health monitoring

Key Technologies Used

🔄 ELT Pipeline

• Fivetran - Automated data extraction and loading
• Airbyte - Open-source data integration platform
• Custom Connectors - Custom data connectors for proprietary systems
• Real-time Streaming - Kafka-based real-time data streaming
• Batch Processing - Scheduled batch data processing

🏗️ Data Transformation

• DBT (Data Build Tool) - SQL-based data transformation
• Data Modeling - Dimensional modeling and data marts
• Data Quality - Automated data quality testing
• Documentation - Automated data lineage and documentation
• Version Control - Git-based version control for data models

🏞️ Data Lake

• DLThub - Data lakehouse architecture
• Delta Lake - ACID transactions on data lakes
• Data Governance - Data catalog and metadata management
• Data Partitioning - Optimized data partitioning strategies
• Data Retention - Automated data lifecycle management

📊 Metrics Store

• Cube.dev - Semantic layer and metrics store
• Metric Definitions - Centralized metric definitions
• API-First Design - RESTful APIs for data consumption
• Caching - Intelligent caching for performance
• Security - Row-level security and access controls

Key Features Built

📥 Data Ingestion

• Multi-Source Integration - 50+ data source integrations
• Real-time Streaming - Sub-second data latency
• Data Validation - Automated data quality checks
• Error Handling - Robust error handling and retry mechanisms
• Monitoring - Real-time pipeline monitoring and alerting

🔄 Data Processing

• ETL/ELT Pipelines - Automated data processing workflows
• Data Transformation - Complex business logic implementation
• Data Aggregation - Multi-level data aggregation
• Data Enrichment - Third-party data enrichment
• Data Deduplication - Automated duplicate detection and removal

📊 Analytics & BI

• Self-Service Analytics - User-friendly analytics interface
• Custom Dashboards - Interactive dashboards and visualizations
• Ad-hoc Queries - SQL-based ad-hoc query capabilities
• Report Automation - Automated report generation and distribution
• Mobile Analytics - Mobile-optimized analytics interface

🔍 Data Discovery

• Data Catalog - Comprehensive data catalog and search
• Data Lineage - End-to-end data lineage tracking
• Data Profiling - Automated data profiling and statistics
• Data Dictionary - Business glossary and data definitions
• Data Governance - Data governance and compliance management

Technical Challenges Solved

⚡ Performance & Scalability

• High Volume Processing - Handle terabytes of data daily
• Real-time Processing - Sub-second data processing latency
• Query Optimization - Optimized query performance for complex analytics
• Auto-scaling - Cloud-native auto-scaling based on demand
• Cost Optimization - Data processing cost optimization

🔒 Data Security & Privacy

• Data Encryption - End-to-end data encryption
• Access Controls - Role-based access controls
• Data Masking - Sensitive data masking and anonymization
• Compliance - GDPR, CCPA, and industry compliance
• Audit Trails - Complete data access audit trails

🔄 Data Quality & Reliability

• Data Quality Monitoring - Automated data quality checks
• Data Lineage - End-to-end data lineage tracking
• Error Handling - Robust error handling and recovery
• Data Validation - Automated data validation rules
• Monitoring & Alerting - Comprehensive monitoring and alerting

Business Impact

• Data Processing Speed - 90% reduction in data processing time
• Analytics Accessibility - 10x increase in self-service analytics usage
• Data Quality - 99.9% data accuracy and reliability
• Cost Reduction - 70% reduction in data infrastructure costs
• Decision Making - Real-time insights for faster decision making

Technologies Used