Loading a 43 MB JSON file caused memory to spike to 808 MB. That is not a quirk of Python; it is a structural problem with how almost every spreadsheet and data tool handles large data today. The file itself is small, but the in-memory representation balloons because the tool builds a complete object model before you can touch a single row. We have seen this pattern for years, and it is the clearest signal yet that the traditional load-everything approach is a bottleneck that no amount of hardware can fix. The practical consequence is straightforward: if your data pipeline depends on loading entire datasets into memory, you are building fragility into your workflow from the start.
Streaming changes the math entirely. Instead of pulling the whole JSON structure into RAM, a streaming parser reads records one at a time, processes them, and discards them. The memory footprint stays close to the size of a single record, not the entire collection. This is not a niche optimization for engineers running petabyte-scale pipelines. It matters the moment your daily spreadsheet crosses a few hundred kilobytes, because the overhead of parsing a complex file into a full object tree often multiplies memory use by ten or twenty times. For anyone building self-service analytics or automated data engines, this distinction determines whether a task completes in seconds or crashes mid-run. We recently explored how teams can Build Self-Service Platforms From Python Simulations Without Breaking Workflows, and the same principle applies here: processing data as a stream rather than a snapshot keeps your infrastructure lean and your failure modes predictable.
The deeper issue is that most spreadsheet tools were designed when a "large file" meant a few thousand rows of tabular data. Today, data arrives as nested JSON, API responses, and event logs that structurally resist the grid metaphor. When you load one of these files into a traditional spreadsheet, the tool tries to flatten and index everything upfront, which is exactly what causes the memory spike. The solution is not to buy more RAM or to compress files differently. It is to adopt a processing model that treats data as a flow rather than a static asset. This connects directly to another observation we have made: there are Four Signals to Know When Your Data Engine Has Outgrown Spreadsheets, and a 20x memory inflation on load is one of the loudest signals you can get.
The takeaway is specific and actionable. If you load data into any tool and see memory usage far exceed the file size on disk, you are paying a tax for the convenience of random access across the entire dataset. Streaming is not a workaround; it is the correct default for any pipeline that processes data from external sources. The next time you write a script or configure a spreadsheet import, ask whether the tool streams or loads. That single question will save you the 808 MB surprise and the debugging session that follows.