📄️ Managing Repos
Once your Arraylake organization has been fully configured and you have
📄️ Manage Zarr Data
Once you have created a repo, you are ready to manage Zarr data.
📄️ Version Control with Arraylake
Arraylake carries over concepts from other version control software (e.g. Git) to multidimensional arrays.
📄️ Use Arraylake with Xarray
In Manage Zarr Data, we saw how to interact with Arraylake using the Zarr python API.
📄️ Searching and filtering
Introduction
📄️ Virtual Datasets
Arraylake's native data model is based on Zarr Version 3.
📄️ Scaling with Arraylake
For larger datasets, users will want to parallelize the reading, processing, and
📄️ Understanding Write Conflicts
Arraylake's version control system means that multiple users can safely collaborate on the same datasets. However, this also means that multiple clients editing the same metadata files or array data objects can give rise to situations where changes to the same objects conflict with one another. This tutorial will illustrate scenarios that give rise to these conflicts. See Transactions and Version Control for a detailed discussion of these concepts.
📄️ Exporting data from Arraylake
Arraylake's CLI tool allows users to smoothly export data from an Arraylake Repo to local files or an object storage destination.