Tutorials (by use-case)
The Get Started path teaches you the mechanics: install, upload, verify, restore, then keep it current with scheduled incremental syncs. These tutorials pick up where that leaves off — they follow a realistic persona through an end-to-end workflow, with concrete dataset sizes, storage-class choices, and cost trade-offs.
Pick your use-case
| If you… | Start here |
|---|---|
| Archive millions of tiny files without a huge S3 request bill | Millions of small files |
| Move large sequencing files (FASTQ / BAM / VCF) off an HPC cluster | Genomics / sequencing data |
| Archive microscopy stacks and screening images without losing pixels | Imaging / microscopy data |
| Version ML datasets and push them through DVC | ML datasets with DVC |
| Run shared buckets for a whole lab and keep spend attributable | Lab data manager |
| Oversee grant budgets and produce sponsor-ready reports | Principal investigator |
Come from rclone or aws s3 cp and want the equivalent commands | Migrating from rclone / aws cli |
How tutorials relate to the rest of the docs
A tutorial owns the narrative — the "why", the numbers, and the sequence of steps for one kind of user. It does not re-document every flag. When a step uses a feature, it links out to the canonical page:
- Guides explain the mechanism (e.g. Compression, Multi-prefix sharding, Tier-aware storage).
- Reference lists every flag (e.g.
upload).
If a tutorial and a guide ever seem to disagree on how a flag behaves, the guide and reference are authoritative.
Before you start
Every tutorial assumes you've done the Quick Start once and have working AWS credentials (AWS setup). The canonical upload command throughout is:
cargoship upload SOURCE_DIR s3://BUCKET/PREFIX/New to the terms upload ID, manifest, shard, or chunk? See Concepts & terminology.