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Analytics Workspace Application
Data EngineeringSelf-service analytics platform allowing non-technical users to upload data and generate insights.
Overview
💡 Challenge
Business users rely on analysts for basic data exploration and reporting, creating bottlenecks.
⚡ Solution
Built a Streamlit application where users upload CSV/Excel files and automatically get descriptive statistics, visualizations, and data quality reports.
🎯 Impact
Democratized data access and reduced analyst workload by 30% for routine requests.
Technical Details
🛠️ Tech Stack
PythonStreamlitPandasPlotlyDuckDB
✨ Key Features
- ✓Multi-format data upload (CSV, Excel, JSON)
- ✓Automated exploratory data analysis
- ✓Interactive visualization builder
- ✓Data quality profiling
Key Learnings
- →User-friendly interfaces require extensive input validation
- →Self-service tools need good documentation and examples
- →Performance optimization is critical for large file handling
📊 Data Notes
This project uses synthetic/open data to demonstrate capabilities while maintaining privacy and confidentiality. All methods and approaches are applicable to real-world scenarios.