zarr-data-format
By uw-ssec
Create, manage, and optimize chunked compressed N-dimensional Zarr arrays for cloud-native scientific workflows. Set up S3, GCS, Azure Blob backends with fsspec and caching; apply numcodecs like Blosc/Zstd; migrate HDF5/NetCDF via xarray; integrate with Dask for scalable analysis.
Component Overview
Install
npx claudepluginhub uw-ssec/rse-plugins --plugin zarr-data-formatComponent Details
Agents (2)
Specialist in integrating Zarr with cloud object stores (AWS S3, Google Cloud Storage, Azure Blob Storage). Expert in storage backend selection (fsspec, obstore, Icechunk), authentication configuration, metadata consolidation for cloud performance, and cloud-specific Zarr optimization. Use this agent when the user asks to "store zarr on S3", "read zarr from GCS", "configure azure blob for zarr", "set up cloud zarr store", "optimize zarr for cloud", "use obstore with zarr", "configure icechunk", or needs cloud-specific Zarr guidance. <example> Context: User needs to set up S3 access user: "I need to read a public Zarr dataset from S3 and write processed results to my own S3 bucket" assistant: "I'll use the zarr-cloud-architect to set up both anonymous read access and authenticated write access to S3." <commentary> Cloud Zarr access requires proper backend configuration, credentials, and potentially different stores for read vs write. </commentary> </example> <example> Context: User choosing between storage backends user: "Should I use fsspec or obstore to access my Zarr data on GCS?" assistant: "I'll invoke the zarr-cloud-architect to compare the backends based on your performance and compatibility requirements." <commentary> Backend selection involves trade-offs between performance (obstore/Rust), ecosystem maturity (fsspec), and feature needs. </commentary> </example> <example> Context: User needs versioned Zarr storage user: "I need ACID transactions and version control for my Zarr data on S3" assistant: "I'll use the zarr-cloud-architect to guide you through setting up Icechunk as your storage engine." <commentary> Icechunk provides versioning, ACID transactions, and time-travel for Zarr data on cloud stores. </commentary> </example>
You are a comprehensive Zarr format expert. You help users create, read, write, manage, and optimize chunked, compressed, N-dimensional arrays using Zarr and its integrations with xarray, Dask, and cloud object stores.
Skills (5)
Configure Zarr stores on cloud object storage services. Covers S3, GCS, Azure Blob backends via fsspec (s3fs, gcsfs, adlfs), the high-performance Rust-based obstore library, Icechunk for versioned cloud storage, authentication patterns, caching strategies, and performance tuning for remote I/O.
Configure and optimize compression for Zarr arrays. Covers all numcodecs compressors (Blosc, Zstd, LZ4, Gzip, LZMA, BZ2), pre-compression filters (Delta, Quantize, FixedScaleOffset, PackBits), codec pipelines, Blosc thread safety, and the trade-offs between compression speed and ratio.
Migrate data between formats with a focus on converting HDF5 and NetCDF datasets to Zarr. Covers xarray-based conversion, direct zarr.copy operations, VirtualiZarr for reference-based virtual Zarr stores, kerchunk for legacy workflows, validation strategies, and batch migration pipelines.
Work with the Zarr array storage format for chunked, compressed, N-dimensional arrays. Covers array creation, hierarchical groups, metadata/attributes, advanced indexing modes, data types, thread/process safety, sharding, and Zarr v2 vs v3 differences.
Integrate Zarr with xarray and Dask for labeled, multi-dimensional scientific data workflows. Covers reading and writing Zarr stores with xarray, append and region-write operations, multi-file virtual datasets, Dask chunk alignment with Zarr chunks, encoding configuration, consolidated metadata, and performance optimization for large-scale analysis.
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Has parse errors
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