vaex

Out-of-core processing with Vaex analyzes datasets larger than available RAM by using memory-mapped DataFrames and lazy evaluation. This lets you interact with billions of rows without loading the entire dataset into memory.

Yes, lazy evaluation processes terabytes of tabular data efficiently by deferring computations until results are needed. Vaex uses virtual columns and out-of-core DataFrames to perform fast aggregations without full data materialization.

The best way to visualize big data without loading it into memory is using scalable visualizations built into Vaex. It applies lazy evaluation and out-of-core processing to enable interactive plotting across billions of rows.

Yes, Vaex works with HDF5 and Apache Arrow formats, alongside CSV and Parquet. This multi-format data support allows you to open massive datasets directly for memory-mapped IO and fast exploration.

You integrate machine learning pipelines with out-of-core DataFrames by using vaex.ml. It provides ML-friendly pipelines that apply lazy evaluation to terabytes-scale tables without requiring full data materialization.

You should use out-of-core DataFrames instead of standard in-memory processing when your dataset exceeds available RAM. If your data fits comfortably in memory, standard processing is sufficient and out-of-core overhead is unnecessary.