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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
Creatives on Contra have earned over $150M and we are just getting started
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Clean and ready to set your business instantly! Complete workflow, he also make apps in every niche 💯 recommend @Yuvraj Singh
CSV preparation — an independent Python workflow for splitting tagged records and mapping columns to a supplied schema.
The synthetic example preserves text IDs and multiline fields, stops on unknown tags, and reports output counts and file hashes. Multi-tag routing is explicit, so extra output rows can be explained rather than mistaken for duplicates.
Code and worked example: https://github.com/sjdoane/csv-tag-splitter
