20 posts
When we released Icechunk 1.0 last July, we declared it production-ready and committed to format stability. Since then, adoption has exceeded our expectations. Teams across weather forecasting, climate science, neuroscience, and AI/ML have pushed Icechunk into scenarios we didn't fully anticipate--r
CEO & Co-founder
Staff Engineer
Earthmover built third-wheel, an open-source tool that rewrites Python wheels to install multiple versions of a library in one environment, enabling cross-version compatibility testing for the Icechunk V2 release.
Xarray Community Developer
A preview of Icechunk 2, featuring node rename, chunk reindexing, rectilinear grids, repository-level metadata, and significant performance improvements with a smooth migration path from Icechunk 1.
Staff Engineer
Zarr Python with Icechunk or Obstore now fully saturates the network between EC2 and S3, achieving the physically maximum possible throughput for reading and writing tensor data in the cloud. Benchmarks compare Zarr, Tensorstore, TileDB, and Parquet stacks across a range of chunk sizes and instance types.
CEO & Co-founder
Earthmover co-organizes the Zarr Summit in Rome, bringing together developers and adopters to advance the open-source cloud-native array format as adoption accelerates across major organizations like ESA, NASA, and NVIDIA.
CEO & Co-founder
An introduction to the WMO FM-301 standard for weather radar data and how open-source tools like Xradar are turning fragmented binary radar files into structured, analysis-ready datasets.
Data Scientist
Icechunk 1.0 is now stable and production-ready, bringing transactional safety, efficient versioning, high-performance Rust-based I/O, and virtual references for HDF5 and NetCDF to cloud-native array storage. The release includes manifest splitting, distributed writes, conflict resolution, and a 30 TB ERA5 sample dataset.
CEO & Co-founder
Weather radar captures rich four-dimensional atmospheric data, but legacy binary formats and fragmented archives make large-scale analysis painfully difficult. A modern, cloud-native data model could unlock radar's vast scientific potential.
Data Scientist
Xarray introduces SeasonGrouper and SeasonResampler, two new Grouper objects that enable custom, overlapping, and variable-length seasonal aggregations without workarounds.
Forward Deployed Engineer
Xarray's labeled, multidimensional data structures can solve common pain points in biological data analysis, from tracking microscopy metadata to managing complex genomic datasets. Adoption has been limited by awareness, technical rough edges, and lack of tool integration, but the community is actively working to change that.
Xarray Community Developer
A practical guide to Icechunk's garbage collection and expiration operations, explaining when and how to safely reclaim storage from unused snapshots and dangling objects.
Staff Engineer
At the 2025 Cloud-Native Geospatial conference, Zarr adoption was surging across the geospatial domain, with Copernicus Sentinel, USGS Landsat, Google Earth Engine, and ESRI ArcGIS all embracing the format for cloud-optimized array data.
CTO & Co-founder
A practical walkthrough of how Icechunk uses transactions and conflict detection to guarantee data consistency when multiple processes write concurrently. The post demonstrates optimistic concurrency control and the rebase workflow using a bank-account transfer example.
Staff Engineer
Why traditional scientific file formats like NetCDF perform poorly on cloud object storage, and how cloud-optimized formats like Zarr and Icechunk solve the problem by separating metadata and chunking data.
Software Engineer
Demystifying how S3 prefix sharding actually works and demonstrating that Icechunk can scale to hundreds of thousands of requests per second, far beyond the single-prefix limit.
Staff Engineer
Earthmover and Development Seed partnered with NASA to pilot Icechunk, an open-source tensor storage engine that enables 100x faster cloud-native data access for archival Earth science datasets without costly data migration.
CEO & Co-founder
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
Software Engineer
Zarr-Python 3.0 is released with full support for the Zarr V3 specification, chunk-sharding for more flexible storage, major performance improvements from a fully asynchronous core, and a modernized extensible codebase.
CTO & Co-founder
Earthmover announces Icechunk, an open-source transactional storage engine for Zarr that brings ACID transactions, time travel, data versioning, and high-performance Rust-based I/O to multidimensional array data in cloud object storage.
CEO & Co-founder
The Zarr-Python project is undergoing a major refactor toward version 3.0, bringing full support for the Zarr V3 specification, new asynchronous APIs for better performance, and a modernized plugin system for codecs and storage backends.
CTO & Co-founder
