vaex

To analyze big data datasets exceeding available RAM, use out-of-core processing with lazy evaluation. This approach memory-maps data formats like CSV, Parquet, and HDF5, enabling fast exploration and computation on billion-row tables without loading them entirely into memory.

Lazy evaluation for out-of-core DataFrames is a mechanism that delays computations until results are explicitly requested. It processes extremely large tabular datasets by using memory-mapped access and virtual columns, avoiding excessive memory consumption during data manipulation.

Yes, you can load and process Parquet and HDF5 files without memory limits using out-of-core DataFrames. This approach supports versatile data loading, filtering, and aggregations across formats like CSV, Parquet, HDF5, and Arrow without exhausting system memory.

To compute statistics on a terabyte-scale dataset quickly, apply out-of-core processing with batch execution. This enables fast exploration and analytics on massive tabular data by utilizing memory-mapped data access and lazy evaluation without loading everything into memory.

Yes, out-of-core dataframe processing supports machine-learning integration. It exposes lazy evaluation and virtual columns to handle extremely large tabular datasets, facilitating scalable data science workflows and large-scale analytics within machine-learning pipelines.

You should not use out-of-core DataFrames for data exploration when your tabular datasets fit comfortably within available RAM. In those cases, in-memory processing tools within the same category may provide faster execution without the overhead of memory-mapped access and lazy evaluation.