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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. Problem:
Many organizations still process invoices manually by reading PDF documents and entering key details (invoice number, vendor, amount, etc.) into systems. This process is slow, error-prone, and difficult to scale, and it also makes it harder to detect duplicate invoices or incorrect totals.
Solution:
This project builds an automated invoice processing pipeline that converts uploaded invoice PDFs into structured data. It uses OCR to extract text, LLMs to identify invoice fields, validation checks to ensure correctness, and Kafka-based event streaming to manage the processing pipeline. The extracted data is stored in PostgreSQL and visualized through a dashboard, enabling faster, scalable, and more reliable invoice processing.
