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zarr-python’s performance paradox Last month, we released Zarr-Python 3.0 - a ground-up rewrite of the library (read more about it in this post). Beyond the exciting new features in Zarr V3, we put a lot of work into addressing some long standing performance issues with Zarr-Python 2. With the improvements described in this blog post, we’ve achieved a 14x speedup in loading the ARCO ERA5 dataset! Zarr-Python 2 had a paradoxical performance quirk; although the library could generate massive petabyte-scale datasets, it struggled to perform well when managing large or highly nested hierarchies. For example, listing the contents of a large Zarr group could be painfully slow, particularly if that Zarr group was stored on a high latency storage backend. Zarr users would experience this as long…

Note: This post was originally published on the Zarr developer blog. After more than a year of development, we’re thrilled to announce the release of Zarr-Python 3! This major release brings full support for the Zarr v3 specification, including the new chunk-sharding extension, major performance enhancements, and a thoroughly modernized codebase. Whether you use Zarr to managing large multi-dimensional datasets in the cloud or for high-performance machine learning applications, we’ve built Zarr-Python 3 to help you. Let’s dive into some of the details of this release! Zarr-Python 3 is available today on PyPI and Conda-Forge. It is compatible with Python 3.11 and above. pip install --upgrade zarr # or conda install --channel conda-forge zarr Support for Zarr’s v3 specification The most n…

TLDR We are excited to announce the release of the Icechunk storage engine, a new open-source library and specification for the storage of multidimensional array (a.k.a. tensor) data in cloud object storage. Icechunk works together with Zarr, augmenting the Zarr core data model with features that enhance performance, collaboration, and safety in a multi-user cloud-computing context. With the release of Icechunk, powerful capabilities such as isolated transactions and time travel, which were previously only available to Earthmover customers via our Arraylake platform, are now free and open source. Head over to icechunk.io to get started! This is a blog version of a webinar that took place on October 22, 2024. View the presentation slide deck or check out the video of that webinar: The …

This is a blog version of a webinar that took place on August 27, 2024. Here’s a video of that webinar: Geospatial datasets representing information about real-world features such as points, lines, and polygons are increasingly large, complex, and multidimensional. They are naturally represented as vector data cubes: n-dimensional arrays where at least one dimension is a set of vector geometries. The Xarray ecosystem now supports vector data cubes thanks to Xvec, a package designed for working with vector geometries within the Xarray data model 🎉. For those familiar with GeoPandas, Xvec is to Xarray as GeoPandas is to Pandas. This blog post is geared toward analysts working with geospatial datasets. We introduce vector data cubes, discuss how they differ from raster data cubes, and d…