Where this applies
Specific problems, and how I approach them.
Financial Services Analytics
What I built for my own market analysis, and what it demonstrates.
The problem
Market data is scattered across sources and formats, and pulling it together by hand doesn't scale.
The approach
queryForge pulls from the OpenBB API and provides:
- Real-time stock and cryptocurrency quotes
- Interactive charting with multiple visualization types
- JSON API for programmatic access
- Historical data analysis and trends
Technologies Used
Django • FastAPI • PostgreSQL • Redis • OpenBB API • React
What it does
- Real-time market insights
- Multiple chart types for analysis
- RESTful API for integrations
- Redis caching
- CI/CD via GitHub Actions
Key Features
- Multi-year historical analysis
- Anomaly detection algorithms
- Custom alerting rules
- Trend forecasting
- Performance dashboards
Monitoring Dashboard
(Visualization preview)
Operations Monitoring & Analytics
Transform years of monitoring data into actionable operational insights.
The Challenge
Organizations accumulate vast amounts of monitoring data but struggle to extract meaningful patterns and predict potential issues before they occur.
The approach
Built on years of monitoring data:
- Historical trend analysis and pattern recognition
- Predictive analytics for proactive monitoring
- Custom Python-based data cleaning pipelines
- Automated reporting and alerting systems
Technologies Used
Python • Pandas • Scikit-learn • PostgreSQL • Power BI • Unit Testing
Data Lake to Warehouse Pipeline
Efficient data movement from raw storage to analytics-ready formats.
The Challenge
Organizations need to process and transform massive datasets from data lakes into structured formats optimized for analytics and reporting.
The approach
queryLane provides:
- Hadoop/Hive/Spark ecosystem for big data processing
- Scalable ETL pipelines with data validation
- Schema evolution and versioning
- Incremental data loading strategies
Technologies Used
Apache Hadoop • Apache Hive • Apache Spark • Python • PostgreSQL
Pipeline Architecture
↓ Extract & Validate
↓ Transform & Clean
↓ Load & Optimize
(queryForge, Power BI, Python/R)
Benefits
- Data quality assurance built-in
- Fast query performance
- Cost-optimized storage
Custom Analytics Platforms
Full-stack builds when the workflow needs software of its own.
Full-Stack Development
End-to-end platform development using:
- Django frontend framework
- FastAPI for high-performance APIs
- React for interactive components
- PostgreSQL with optimization
- Redis for caching & sessions
Integration Ready
Connect to your existing systems:
- RESTful API endpoints
- External API integrations
- Database connections
- Authentication systems
- Third-party services
Production Quality
Build standards:
- Unit testing
- CI/CD pipelines (GitHub Actions)
- Documentation & code comments
- Scalable architecture
queryForge: how I built it
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