Use streaming or chunked processing instead of loading everything into memory. Apply generators or iterators to read data lazily. Use out-of-core libraries such as Dask, Vaex, or DuckDB for large-scale processing. Store and access numerical data with memory-mapped arrays (e.g., NumPy memmap). Move data to a database or columnar storage format and query only what you need. Redesign algorithms for incremental or batch computation (e.g., partial_fit in ML). Optimize data representation using efficient data types to reduce memory footprint.