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UID:pretalx-pydataglobal2025-AAGRYV@cfp.pydata.org
DTSTART:20251209T163000Z
DTEND:20251209T170000Z
DESCRIPTION:TIFF\, HDF5\, and Zarr represent a few choices to store large n
 -dimensional arrays which represent scientific and machine learning data. 
 Trade-offs have to be considered when selecting one of these formats. Whil
 e TIFF files are recognized by many applications particularly for imaging\
 , they are limited in the number of dimensions\, two\, traditionally\, or 
 three in the case of GeoTIFF. HDF5 was created to support hierarchical sci
 entific data with arrays up to 32 dimensions\, but are mainly readable by 
 scientific applications. Neither TIFF nor HDF5 were designed with the clou
 d in mind. Meanwhile\, Zarr reimagined HDF5 in the era of cloud computing 
 and key-value object stores. In retrospect\, these disparate formats have 
 many similarities. I will demonstrate how to take advantage of these simil
 arities to combine the formats and make data accessible to a wide range of
  local and cloud-based application without duplicating the data itself.
DTSTAMP:20260612T011743Z
LOCATION:Data Engineering & Infrastructure
SUMMARY:Combining Zarr\, HDF5\, and TIFF into a single data format - Mark K
 ittisopikul\, Ph.D.
URL:https://cfp.pydata.org/pydataglobal2025/talk/AAGRYV/
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