> For the complete documentation index, see [llms.txt](https://docs.redivis.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.redivis.com/reference/datasets/create-and-edit-datasets/dataset-lifecycle.md).

# Dataset lifecycle

## Create a new version

When it's time to update a dataset's data, you can create a new version. To do this, navigate to the dataset editor and select **Create next version.**

Before this version is released, it will be tagged as `next`. Only dataset editors will be able to see the `next` version on the dataset page and use it in their workflows.

{% hint style="info" %}
A dataset can have up to 1,000 versions. If your use case exceeds this limit, consider creating a new dataset that imports the previous dataset's tables once this limit has been reached.
{% endhint %}

<div data-with-frame="true"><img src="/files/5jIThwYk34DmyBbVxrTE" alt=""></div>

## Update existing tables

In your new version, you may have a combination of:

* Table additions (new in this version)
* Table deletions (no longer exist in this version)
* Table modifications (the table still exists, but its data has changed).

If you have table modifications, it is strongly recommended to *not* delete and recreate the table. This is inefficient from a storage perspective, and breaks the table's lineage across versions. Instead, you should [import your new data](/reference/datasets/create-and-edit-datasets/import-sources.md) directly to the existing table.

When importing data to an existing table, you can choose whether these uploads should be appended to, or fully replace, the contents of the existing table. The latter will functionally achieve the same outcome as deleting and recreating the table, but Redivis will be able to more efficiently store the data, and the table will be linked across its versions.

## Release

Once you are ready to make your updates available to non-editors, you will need to release the version.

As soon as it is released, it will be accessible to all who meet its access rules.

## Unrelease

If the most recent version of a dataset has been released in the last 7 days, and there is no `next` version created yet, you have the option to unrelease it.

This will revert the dataset to the exact state it was in before the version was released. If anyone who is not a dataset editor has this version in a workflow, they will lose access to the data, though they can revert to a previous version if it exists.

## Version storage

All versions of a dataset contribute to that dataset's total size, which in turn will count towards your [usage quotas](/reference/your-account/compute-credits-and-billing.md) or [organization billing](/reference/billing.md), depending on whether the dataset is owned by you or an organization.

This total size will be displayed in the dataset editor, alongside the size for the current version. For datasets with one version, this total size may be slightly larger than the current version, as Redivis stores metadata behind the scenes to support future versioning.

As new versions are created, Redivis efficiently computes row-level differences between versions and only additions and updates to existing data will contribute to the dataset's total storage size. This prevents data that is consistent across versions from being double-counted.

Adding, removing, or changing column types won’t affect row uniqueness, as the underlying storage represents all values as strings. Only the storage size of the new column is added.

## Archival

Datasets on Redivis are intended as a persistent store of information, and this persistence is critical to ensure the reproducibility of analyses. However, in some cases, it may be necessary to stop or pause usage of a dataset (or specific versions) due to incorrect information or to reduce costs.

**Archival** stops all usage, including reading, querying, and exporting of data. However, no data is removed from Redivis, and full functionality can be restored at any time. No other aspects of metadata, or access are changed when a dataset is archived.

### Version archival

Specific versions within a dataset can be archived, as long as they are not the `current` or `next` version. Archiving a version preserves all of its contents, while stopping usage of its data.

When a version is archived, all derivative tables in researchers' workflows are archived as well. These tables can be reconstituted when the data is unarchived, or the user can update their workflow to use a different version and rerun their analysis.

To archive a version, navigate to the dataset's version history and open the action menu in the top right corner of any version. You can also shift-click to select multiple versions to archive or unarchive simultaneously.

### Dataset archival

Specific versions within a dataset can also be archived, as long as they are not the `current` or `next` version. Archiving a version preserves all of its contents, while stopping usage of its data.

Note when a dataset is archived, all derivative tables in researchers' workflows are archived as well. These tables can be reconstituted when the dataset is unarchived, or the user can update their workflow to use a different dataset and rerun their analysis.

To archive a dataset, navigate to the dataset's settings in the editor, and select **Archive.** You can also bulk archive datasets by selecting multiple datasets and choosing the archive action. Datasets with an unreleased (`next`) version cannot be archived.

### Storage classes

When archiving data, you can select one of two storage classes:

* **Standard storage** will keep the data stored as it is, and storage costs will remain unchanged. Archived data in standard storage can be moved to cold storage at any time.
* **Cold storage** will move the data to cheaper, long-term storage, and will compress any tabular data prior to archival. Data in cold storage incurs a significantly lower [monthly storage cost](/reference/billing.md), but there is a [fee](/reference/billing.md#archival) to unarchive it.

Accounts must have billing configured in order to utilize cold archival.

{% hint style="info" %}
If you are cold archiving a version to reduce storage costs, be aware that Redivis stores data efficiently across versions. The storage used by a particular record will be archived *only* if it is unique to the archived version (or, if archiving a series of versions, if that record doesn't exist in any non-archived versions). This is shown as the number of "discrete bytes" when selecting versions for archival.
{% endhint %}

### Unarchival

Unarchiving a version or dataset makes the data usable again. You can unarchive any dataset or version by navigating to the dataset's settings and selecting the **Unarchive** option.

Unarchival of data in standard storage is free and instantaneous.

Unarchival of data in cold storage incurs a one-time [unarchival fee](/reference/billing.md) and can take up to several minutes for larger datasets.

## Deletion

Datasets on Redivis are intended as a persistent store of information, and this persistence is critical to ensure the reproducibility of analyses. However, in some cases it may be necessary to fully delete a dataset (or specific versions) due to license requirements, administrative error, or to reduce costs.

**Deletion** permanently removes data, while preserving a reference to the deleted dataset or version for citation purposes.

### Version deletion

Any version of a dataset can be deleted, as long as it is not the currently released version. Deleting a version will delete all metadata and data associated with it.

A deleted version will no longer be available for analysis, and any derivative workflow tables that reference the version will be marked as archived, meaning they can no longer by read or analyzed. In order to continue working with the dataset, researchers will need to update their workflows to use a non-deleted version.

To delete a version, navigate to the version modal and open the action menu in the top right corner of any version. You can also shift-click to select multiple versions to archive or unarchive simultaneously.

{% hint style="info" %}
If you are deleting versions to reduce storage costs, be aware that Redivis stores data efficiently across across versions. The storage used by a particular record will be archived *only* if it is unique to the archived version (or, if archiving a series of versions, if that record doesn't exist in any non-archived versions). This is shown as the number of "discrete bytes" when selecting versions for archival.
{% endhint %}

### Dataset deletion

In some situations it may be necessary to fully delete a dataset. Once deleted, the dataset will no longer be discoverable, though it will still show up in users' workflows that reference the dataset, and bookmarked URLs and DOIs will still resolve to the dataset's landing page. You can also view a list of deleted datasets by navigating to your workspace or organization administrator panel, and filtering datasets by `status: deleted`.

To ensure future reproducibility, dataset metadata and documentation is preserved upon deletion. However, all data will be expunged and the dataset will no longer by usable.

The dataset's public [access level](/reference/data-access/access-levels.md) will be persisted in its deleted state, meaning that if the dataset was previously visible, it will still be visible, but not discoverable, once deleted. Additionally, any users who had access to the dataset prior to deletion will have their access persisted. Though notably, the dataset will be emptied of all data and authorized researchers will only be able to view metadata. These default access rules can be modified by navigating to the deleted dataset, reconfiguring access accordingly.

To delete a dataset, select **Delete** on the dataset settings tab of the dataset editor. You can also right-click on the dataset when viewing the list of your datasets.

### Undeletion

If you delete an unreleased version or dataset, the deletion is permanent and cannot be undone. If you delete a released version or dataset, there is a 7-day window within which the dataset or version can be undeleted. After 7 days, deletion becomes permanent and the data will be unrecoverable.


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