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UID:pretalx-pyconde-pydata-2024-93MHQ3@pretalx.com
DTSTART;TZID=CET:20240423T141000
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DESCRIPTION:A key feature of the Python data ecosystem is the reliance on s
 imple but efficient primitives that follow well-defined interfaces to make
  tools work seamlessly together (Cf. http://data-apis.org/). NumPy provide
 s an in-memory representation for tensors. Dask provides parallelisation o
 f tensor access. Xarray provides metadata linking tensor dimensions. **Zar
 r** provides a missing feature\, namely the scalable\, persistent storage 
 for annotated hierarchies of tensors. Defined through a community process\
 , the Zarr specification enables the storage of large out-of-memory datase
 ts locally and in the cloud. Implementations exist in C++\, C\, Java\, Jav
 ascript\, Julia\, and Python\, enabling.\n\nThis talk presents a systemati
 c approach to understanding the newer [Zarr Specification Version 3](https
 ://zarr-specs.readthedocs.io/en/latest/v3/core/v3.0.html) by explaining th
 e critical design updates\, performance improvements\, and the lessons lea
 rned via the broader specification adoption across the scientific ecosyste
 m.\n\nI will also briefly discuss the evolution of the Zarr - the developm
 ent of the [Zarr Enhancement Process (ZEP)](https://zarr.dev/zeps) and its
  use to define the next major version of the specification (V3)\; as well 
 as uptake of the format across the research landscape.
DTSTAMP:20260514T014304Z
LOCATION:A1
SUMMARY:Exploring Zarr: From Fundamentals to Version 3.0 and Beyond - Sanke
 t Verma
URL:https://pretalx.com/pyconde-pydata-2024/talk/93MHQ3/
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