For small companies, running Spark on a minimal Fabric F2 guarantees throttling. To ingest big data on a strict budget, you must drop the distributed overhead entirely. This session details the shift to optimized, single-node Python ingestion. Using real-world data, I will show how modern Python libraries use lazy evaluation and streaming for strict memory management. The takeaway: you can build a highly capable data engineering workflow on a minimal budget without significant downsides.