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Problem:
Financial data from stocks and crypto APIs was scattered, refreshed manually, and not ready for analytics or ML use.
Solution:
Built an Apache Airflow pipeline to collect, transform, validate, and load real-time financial data from multiple APIs into PostgreSQL, MongoDB, AWS RDS, and Qdrant.
Tools:
Apache Airflow, Python, PostgreSQL, MongoDB, AWS RDS, Qdrant, APIs
Result:
Automated sub-hourly data refresh, processed thousands of records daily, and delivered clean data for dashboards, analytics, and vector search.
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
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Title
Real-Time Stock & Crypto Data Pipeline Automation
About This Project:
This project demonstrates an end-to-end production-grade data engineering solution for processing real-time financial market data.
The pipeline automatically collects stock and cryptocurrency data from multiple financial APIs, orchestrates ETL workflows with Apache Airflow, validates incoming records, and stores processed data across MongoDB, PostgreSQL, AWS RDS, and Qdrant Vector Database.
The architecture is designed for reliability, scalability, and automation while supporting downstream business intelligence dashboards, analytics, and AI-powered vector search applications.
Key Features
• Real-time ingestion from multiple financial APIs
• Apache Airflow workflow orchestration
• Parallel ETL execution
• Automated data validation
• Multi-database architecture
• Production-ready pipeline monitoring
• High-frequency automated data refresh
Business Impact
• Reduced manual data processing
• Automated financial data collection
• Improved reporting speed
• Reliable centralized data storage
• Enabled downstream analytics and machine learning
My Responsibilities
Designed the complete data pipeline architecture
Built Apache Airflow DAGs for workflow orchestration
Developed ETL pipelines for data extraction, transformation, and validation
Integrated multiple financial APIs
Implemented automated data quality checks
Stored processed data in multiple databases
Optimized scheduling for reliable execution
Built scalable architecture for analytics and ML workloads
Outcome / Results:
The solution automated the complete financial data pipeline, reducing manual intervention while enabling sub-hourly data refreshes. It supports thousands of records per day across multiple storage systems with automated validation, making the data immediately available for dashboards, business analytics, and AI applications.
App Title: EcoFill Designer
Stated Category: 📊 Dashboard / 🛒 Marketplace (This perfectly matches two eligible categories!)
What the App Does: EcoFill provides crucial circular economy infrastructure for the textile and logistics industries. It features an automated calculator that instantly translates variable cotton scrap dimensions into volume metric requirements (Liters) and dynamically connects companies directly to localized B2B recycling hubs to eliminate landfill waste.
How I Built on the AI Base: * AI Foundation: The Bubble AI builder effectively laid the groundwork for our initial visual cards and page templates.
Taking the Wheel (Custom Functionality): I built custom mathematical expressions directly into the relational database workflows (L * W * H / 1000) to dynamically save precise metrics, integrated custom conditional filters into the repeating group marketplace grid based on material type and location regions, and fully constructed a backend data export-to-CSV architecture.
The Live Page link: https://losan629060o.bubbleapps.io/version-test/marketplace
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