the loading time (from an nvme drive, Ubuntu 18.04, python 3.6.9, numpy 1.19.5) for each file is listed below:
0.179s eye1e4.npy (mmap_mode=None) 0.001s eye1e4.npy (mmap_mode=r) 0.718s eye1e4_bjd_raw_ndsyntax.jdb 1.474s eye1e4_bjd_zlib.jdb 0.635s eye1e4_bjd_lzma.jdb
clearly, mmapped loading is the fastest option without a surprise; it is true that the raw bjdata file is about 5x slower than npy loading, but given the main chunk of the data are stored identically (as contiguous buffer), I suppose with some optimization of the decoder, the gap between the two can be substantially shortened. The longer loading time of zlib/lzma (and similarly saving times) reflects a trade-off between smaller file sizes and time for compression/decompression/disk-IO.
I think the load time for mmap may be deceptive, it isn't actually loading anything, just mapping to memory. Maybe a better benchmark is to actually process the data, e.g., find the mean which would require reading the values.