I'm Nazim, founder of Koinju. We've been ingesting per-exchange trade data for many years (now 286B trades, 25 exchanges) for an institutional product. We just opened our DB via direct SQL access. We offer a Free tier: 50 queries/month to test the product.
I'd love any feedback on SQL vs REST framing. Most of what people write in pandas/polars scripts to aggregate trades for something that could be very simple to do in direct SQL. Cross-exchange index calculation went from 2,500 lines of Python to 7 lines of SQL. Multi-exchange OHLCV: 130 lines to 3. And we added multiples exemples in our doc : https://docs.koinju.io/compute-engine/introduction
Happy to answer anything, schema, query plan, ingestion architecture, why sql not rest, what we don't have, what we got wrong.
Thanks!
Direct SQL access to over 286B crypto trades · HackerTrans
I'm Nazim, founder of Koinju. We've been ingesting per-exchange trade data for many years (now 286B trades, 25 exchanges) for an institutional product. We just opened our DB via direct SQL access. We offer a Free tier: 50 queries/month to test the product.
I'd love any feedback on SQL vs REST framing. Most of what people write in pandas/polars scripts to aggregate trades for something that could be very simple to do in direct SQL. Cross-exchange index calculation went from 2,500 lines of Python to 7 lines of SQL. Multi-exchange OHLCV: 130 lines to 3. And we added multiples exemples in our doc : https://docs.koinju.io/compute-engine/introduction
Happy to answer anything, schema, query plan, ingestion architecture, why sql not rest, what we don't have, what we got wrong.
Thanks!