vaex

You can process large tabular datasets exceeding RAM by using out-of-core DataFrame operations. This approach reads data from disk in chunks, enabling fast aggregations and statistical computations on billions of rows without loading everything into memory.

Lazy evaluation in big data processing computes operations only when results are explicitly needed. Instead of executing transformations immediately, it builds a computational graph, saving memory and optimizing performance for large datasets.

Yes, you can build machine learning pipelines on large datasets without memory constraints by using out-of-core operations. This allows model training and feature engineering directly on massive tabular data that exceeds available RAM.

The best way to visualize large datasets without sampling is using out-of-core DataFrame tools designed for big data. These tools perform efficient visualization by computing aggregations on the fly, allowing you to plot billions of rows interactively.

Yes, out-of-core processing supports fast aggregations on CSV files. By reading large CSV files directly from disk and using lazy evaluation, you can quickly compute statistics like means and distributions across billions of rows.

You should avoid out-of-core DataFrame operations when working with small datasets that fit comfortably into RAM, as standard in-memory processing will be faster due to reduced disk I/O overhead.