Simple Queue Can Save Your Pipeline: DuckDB + Python

If you’ve ever tried to shove millions of rows into a database, you know the pain, slow inserts, blocked threads, and that sinking feeling when your pipeline just can’t keep up. I recently worked on a minimal pipeline design that tackles this problem head-on, and I think it’s worth sharing, especially if you’re building a bronze layer for data lake. For small to medium scale workloads (think tens of millions of rows), this pipeline is enough. It’s simple, easy to maintain, and doesn’t require spinning up distributed infrastructure. DuckDB is surprisingly capable here. You can access the repo here: https://github.com/meemeealm/Multithreaded-Ingestion-Pipeline.git The Idea The pipeline is built around a producer-consumer model . Instead of one big monolithic process, we split responsibilities: Producer (Shredder Thread): Reads Parquet rows, batches them, and pushes them into a queue. Queue (Thread-safe buffer): Acts as the middleman. It smooths out the flow and prevents the producer from