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HDF5 is the scientific community's answer to "how do I store a petabyte of telescope data with full metadata." The format organises data hierarchically (like a filesystem within a file), supports datasets of arbitrary size, includes self-describing metadata, and handles multidimensional arrays natively. It's the standard for astronomy, genomics, climate science, and machine learning model weights.
A single HDF5 file can contain thousands of datasets organised in groups (like folders), each with attached metadata (units, dimensions, provenance). The format supports partial I/O — you can read one variable from a 100 GB file without loading the rest. Compression is built in, and parallel I/O allows multiple processes to read/write simultaneously.
Python's h5py and HDFView (free GUI) are the standard tools. NumPy arrays map directly to HDF5 datasets. PyTorch and TensorFlow use HDF5 for model serialisation (though both are moving toward their own formats). For ML practitioners, HDF5 is the format your pre-trained model weights might arrive in.