# Best practices for data stored on cloud?

**URL:** https://discuss.dvc.org/t/best-practices-for-data-stored-on-cloud/1448
**Category:** Questions
**Created:** [January 4, 2023, 2:10pm UTC](https://discuss.dvc.org/t/best-practices-for-data-stored-on-cloud/1448 "2023-01-04T14:10:10Z")
**Posts on this page:** 1
**Page:** 1

<div class="post-metadata">

### Author: ![nael](https://avatars.discourse-cdn.com/v4/letter/n/4bbf92/32.png) [@nael](https://discuss.dvc.org/u/nael)
#### Post date: [January 4, 2023, 2:10pm UTC](https://discuss.dvc.org/t/best-practices-for-data-stored-on-cloud/1448/1 "2023-01-04T14:10:10Z")

</div>

Hi!  
I wanted to get some advice on what are the best practices for using DVC for the following use case:

- Data will be stored on S3 buckets and updated regularly. The previous data is not changed, only updated with new entries.
- Every time the data is updated, we will run some analysis and train a model on this data. This should be done on the cloud as well.
- Data size will be in the order of hundred GBs. Will this cause problems regarding caching and data duplication? Should reflinks be used in this case?
- We are intending to use DVC to track the data versions used for each experiment (analysis and models) and have the option to rollback to previous versions.
- Nothing will be executed locally.

What would be the recommended DVC flow for this pipeline?  
Please let me know if you’d like more clarifications.

Thanks!