Finance Data Analytics — End-to-End Automation
- Project: Finance Data Analytics — End-to-End Automation
- Focus: API Data Ingestion · ETL Pipelines · Automated Publishing · Analytics Dashboards
- Demo (Streamlit): Live Demo
- Source Code:
Project Overview
This project implements a fully automated financial data analytics workflow, covering API-based data ingestion, ETL pipeline design, feature engineering, aggregation, and analytics-ready data publishing. The system is designed for reproducibility, modularity, and downstream consumption by multiple visualization tools.
Processed datasets are published on a schedule and consumed by interactive dashboards, enabling rapid iteration, exploratory analysis, and comparative visualization across different front-end frameworks.
What I Built
- Automated ingestion pipelines for external financial data sources
- ETL workflows for cleaning, transforming, aggregating, and publishing analytics-ready datasets
- Versioned outputs that support reproducibility and downstream comparisons
- An interactive dashboard layer for filtering, monitoring, and exploratory analysis
Why It Matters
This project reflects a systems-oriented side of my work: turning messy, continuously updated source data into stable analytical products. It combines engineering concerns such as scheduling, reliability, and reproducibility with the usability goals of dashboarding and business-facing analytics.
Interactive Demo (Streamlit)
The embedded dashboard below demonstrates real-time consumption of periodically published analytics data, supporting exploratory inspection, filtering, and summary visualization.
Note: The demo is unavailable between 2:00–7:00 AM EST for cost control. During this window, the instance is intentionally offline.