Our Work
Production-ready solutions demonstrating our expertise
QueryForge Analytics Platform
Full-stack financial data analytics platform with real-time market insights
Project Overview
QueryForge is a production-ready analytics platform designed for financial data analysis. It integrates with the OpenBB API to deliver real-time stock and cryptocurrency data, complete with interactive visualizations and programmatic API access.
Key Features
- Real-time stock and crypto quotes
- Multiple chart types (line, bar, candlestick)
- JSON API responses for integrations
- Redis caching for optimal performance
- Responsive web interface
- RESTful API architecture
Technical Stack
Frontend
Django Templates, React Components, Modern CSS
Backend
Django, FastAPI, PostgreSQL, Redis
External APIs
OpenBB Financial Data API
DevOps
GitHub Actions (CI/CD), Postman (API Testing), Unit Tests
Deployment
DigitalOcean, Production-ready configuration
Live Financial Data Demo
See QueryForge in action with real-time market data
📈 Interactive Stock & Crypto Charts
React component with live data will be integrated here
QueryLane Data Platform
Big data processing platform built on the Hadoop ecosystem
🏗️ Project Description
QueryLane is a comprehensive data lake solution designed for large-scale data ingestion, processing, and storage. Built on the Hadoop/Hive/Spark ecosystem, it provides a robust foundation for big data analytics workflows.
Capabilities
- Multi-terabyte data ingestion
- Distributed processing with Spark
- SQL-like queries with Hive
- Schema evolution support
- Integration with data warehouses
- Data quality validation pipelines
💻 Technical Implementation
Infrastructure
WSL Ubuntu Environment with Hadoop Cluster
Data Processing
- Apache Hadoop (HDFS)
- Apache Hive (Data Warehouse)
- Apache Spark (Processing)
- Python (ETL Scripts)
Use Cases
- Log file analysis
- Time-series data storage
- Data lake architecture
- Batch processing workflows
Monitoring Data Pipeline
Python-based data cleaning and transformation pipeline with comprehensive testing
📊 Project Overview
A production-grade data cleaning pipeline designed to process years of monitoring data. This project demonstrates best practices in data quality, testing, and maintainability.
Key Features
- Automated data validation
- Missing value handling
- Outlier detection
- Data type standardization
- Incremental processing
- Error logging and reporting
Technologies
- Python 3.x
- Pandas for data manipulation
- Pytest for unit testing
- Great Expectations (validation)
- Logging and monitoring
- CI/CD integration
Quality Assurance
✓ Comprehensive unit test coverage
✓ Automated testing in CI/CD pipeline
✓ Data quality metrics and reporting
✓ Documentation and code comments
Additional Projects & Capabilities
📈 Time Series Analysis
Multi-year monitoring data analysis with trend forecasting and anomaly detection.
Python • Pandas • Scikit-learn • Statsmodels
🎨 Power BI Dashboards
Interactive dashboards for business intelligence and operational monitoring.
Power BI • DAX • Data Modeling
🔗 API Development
RESTful APIs with FastAPI for data access and integrations.
FastAPI • PostgreSQL • Redis • OpenAPI
🧪 Data Science Projects
Statistical analysis, predictive modeling, and machine learning implementations.
Python • R • Jupyter • Scikit-learn
Want to Build Something Similar?
Let's discuss how we can apply these proven solutions to your project.