Earthmover shares AI-ready architecture for scientific data archives
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Earthmover shared an architectural approach for making scientific data archives AI-ready: put data in object storage, index it by chunk, and store it co-located with metadata. Earthmover says this is exactly how its product Arraylake works. The report says that after NASA adopted this approach, common queries achieved a 100x speedup; when extracting a single-point GOES-16 time series, data read volume dropped by 576x. The backdrop is that most scientific data is stored in files not optimized for AI, and frontier labs have begun to pay attention to this problem; Anthropic recently donated to multiple scientific data archives.
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- Hacker News AIHow to make scientific data archives AI-ready
The article argues that scientific data archives are poorly suited to AI use because data is locked in opaque files, and proposes a four-step architecture: place data in scalable object storage, index chunks rather than filenames, co-locate dataset-level metadata with the data in object storage, and catalog repositories rather than individual files. It says this approach lets agents and humans quickly find and fetch only the chunks a query needs, citing a 576x reduction in data fetched for a GOES-16 timeseries and a 100x speedup in common NASA queries. The piece describes this as how Earthmover's Arraylake works, built on Icechunk and Zarr, and notes the same recommendations apply to humans and AI alike.
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