---
title: Work with assets
description: How to work with individual catalog assets, including metadata and feature lists, view relationships and version history, and create snapshots.

---

# Work with assets {: #work-with-assets }

When you add a dataset, DataRobot ingests the source data and runs [EDA1](eda-explained#eda1) to register the asset and make it available from the catalog.

![](images/catalog-16.png)

This page describes how you can interact with your data once it's registered in DataRobot:

* [Update asset details (name, tags, and descriptions).](#view-asset-information)
* [View and create feature lists.](#view-and-create-feature-lists)
* [View and manage configured relationships.](#manage-relationships)
* [View version history.](#view-version-history) 
* [Add comments and have discussions within individual assets.](#add-comments)
* [Create a snapshot of a dynamic dataset.](#create-a-snapshot)
- [Keep data up-to-date by scheduling snapshots.](snapshot)
- [Create a project from a catalog asset.](#create-a-project)

See also: 

- [Add data to the AI Catalog](catalog)
- [Understand asset states](asset-state)
- [Schedule snapshots](snapshot)

Additionally, when Composable ML is enabled, you can [save blueprints to the AI Catalog](cml-catalog). From the catalog, a blueprint can be edited, used to train models in compatible projects, or shared.

![](images/catalog-24.png)

## Find existing assets {: #find-existing-assets }

Once in the **AI Catalog**, there are a variety of tools to help quickly locate the data assets you want to work with. You can:

=== "Search"

    Search for a specific asset using the search query box.

    ![](images/catalog-11.png)

=== "Sort"

    Use the dropdown to modify the order of all existing assets.

    ![](images/catalog-27.png)

    The default sort option is **Creation date**, except after searching for a specific asset, in which case the default is **Relevance**.

=== "Filter"

    Under the search query box, you can filter assets by **Source**, **Tags**, and/or **Owner**.

    ![](images/catalog-12.png)

    For example, you can filter by any [tags](#asset-details) manually added to an asset:

    ![](images/catalog-13.png)


!!! warning "Disable Elasticsearch"
    If you are experiencing performance issues or unexpected behavior in the AI Catalog search, contact your DataRobot representative or administrator for information on disabling Elasticsearch.

    **Feature flag:** Disable ElasticSearch For AI Catalog Search

## View asset information {: #view-dataset-information }

The **Info** tab displays an overview of the asset's details as well as metadata.

![](images/catalog-det-1.png)

&nbsp;  | Element | Description
---|---|---
![](images/icon-1.png) | Name | Name the asset. By default, this is the file name uploaded.
![](images/icon-2.png) | Description | Enter a helpful description of the asset.
![](images/icon-3.png) | Tags | Add tags to help when filtering assets in the AI Catalog. DataRobot offers any predefined tags that match the characters you entered. Select one by clicking or continue typing to add a new tag of alphanumeric characters (special characters and symbols are invalid). Either click outside the entry box or in the dropdown to add the tag.
![](images/icon-4.png) | Overview | An overview of the asset, including the full row count, feature count, and feature types.
![](images/icon-5.png) | Metadata  | Additional metadata, including size, owner, and dataset ID.

Click the pencil icons (![](images/icon-rename.png)) to change the asset name, add a description, or add tags to aid in filtering, and then click anywhere outside of the box to save the change.

![](images/catalog-15.png)

## Profile your data {: #profile-your-data }

The **Profile** tab allows you to preview dataset column names and row data. It can be useful for finding or verifying column names when writing Spark SQL statements for [blended datasets](spark#create-blended-datasets).

!!! note "Info tab vs. Profile tab"
    The **Info** tab displays the data's total row count, feature count, and size.
    
    The **Profile** tab only displays a preview of the data based on a 1MB raw sample, and the feature types and details are based on a 500MB sample.

    Meaning the row count observed on the **Profile** tab may not match that displayed in the **Info** tab.

Note that the preview is a random sample of up to 1MB of the data and may be ordered differently from the original data. To see the complete, original data, use the [**Download Dataset**](#download-datasets) option.

To preview a dataset, select it in the main catalog and click the pencil icon (![](images/icon-pencil.png)) to access dataset information (if available).

1. Click the **Profile** tab to preview the contents of the dataset:

    ![](images/catalog-17.png)

2. Use the **Columns** dropdown to select the number of columns to display on the page and the scroll bars to scroll through those columns. Additionally, you can use the **Rows** dropdown to cycle through available data, 20 rows at a time.

The **Profile** tab also displays details for all features in the dataset. To view details for a particular feature, scroll to it in the display and click. The **Feature Details** listed in the right panel update to reflect statistics for the feature. (These are the same statistics as those displayed on the [**Data**](histogram) for EDA1.)

![](images/catalog-18.png)

## View and create feature lists {: #view-and-create-feature-lists }

You can create new lists and feature transformations for features of any dataset in the catalog. To work with the tools, select the dataset in the main catalog and **Feature Lists** in the left panel.

!!! note
    To create feature lists, you must have Owner or Editor access to the dataset.

When you create feature lists, they are copied to a project upon creation. You can then set the list to use for the project from the **Feature List** dropdown at the top of the **Project Data** list. See the section on working with [**Feature Lists**](feature-lists) for complete details on creating, modifying, and understanding these lists.

![](images/catalog-20.png)

The **Feature List** tab also provides access to a tool for creating variable type feature transformations. While DataRobot bases variable type assignments on the values seen during EDA, there are times when you may need to change the type. Refer to [feature transformations](feature-transforms) documentation for complete details.

To create a feature list:

1. Use the checkboxes to the left of feature names to select a set of features.

2. Click the **Create new feature list from selection** link, which becomes active after you select the first feature.

    ![](images/catalog-22.png)

3. In the resulting dialog, provide a name for the new list and click **Submit**. The new list becomes available through the dropdown.

You can delete or rename any feature list you created. You cannot make any changes to the DataRobot [default feature lists](feature-lists#automatically-created-feature-lists).

![](images/catalog-23.png)

## Manage relationships {: #manage-relationships }

DataRobot’s Feature Discovery capability guides you through creating relationships, which define both the included datasets and how they are related to one another. The end product is a multitude of additional features that are a result of these linkings. The Feature Discovery engine analyzes the included datasets to determine a feature engineering “recipe” and, from that recipe, generates secondary features for training and predictions. Once these relationships are established, you can view them from the catalog.

To view relationships, select the dataset in the main catalog and click the **Relationships** tab to view, modify, or delete existing relationships:

![](images/catalog-19.png)

See complete details of working with [relationships](fd-overview#define-relationships) before modifying relationship details.

## View version history {: #view-version-history }

The **Version History** tab lists all versions of a selected asset. The **Status** column indicates the snapshot status&mdash;green if successful, red if failed, gray if the original version did not have a snapshot.

![](images/catalog-21.png)

Click a version to select it. Once selected, you can create a project from the version and download or delete the contents.

## Add comments {: #add-comments }

The **Comments** tab allows you to add comments to&mdash;even host a discussion around&mdash;any item in the catalog that you have access to. Comment functionality is available in the **AI Catalog** (illustrated below), and also as a model tab from the Leaderboard and in [use case tracking](value-tracker). With comments, you can:

* Tag other users in a comment; DataRobot will then send them an email notification.
* Edit or delete any comment you have added (you cannot edit or delete other users' comments).

![](images/catalog-26.png)

!!! note "Versioning snapshot assets"
	Static assets can only be versioned by uploads of the same type; datasets created by local files are versioned from local file uploads, and datasets created from a data stage are versioned from data stage uploads.

## Create a snapshot {: #create-a-snapshot }

You can uncheck **Create Snapshot** when adding external data connections, to meet certain security requirements, for example. Snapshotted [materialized](glossary/index#materialized) data is stored on disk; [unmaterialized](glossary/index#unmaterialized) data is stored remotely as your asset and only downloaded when needed.

To determine whether an asset has been snapshotted, click on its catalog entry and check the details on the right. If it has been snapshotted, the last snapshot date displays; if not, a notification appears:

![](images/catalog-7.png)

To create a snapshot for unmaterialized data:

1. Select the asset from the main catalog listing.

2. Expand the menu in the upper right and select **Create Snapshot**.

	![](images/catalog-8.png)

	You cannot update the snapshot parameters that were defined when the catalog entry was added; snapshots are based on the original SQL.

3. DataRobot prompts for any credentials needed to access the data source. Click **Yes, take snapshot** to proceed.

4. DataRobot runs EDA. New snapshots are available from the version history, with the newest ("latest") snapshot becoming the one used by default for the dataset.

Once EDA completes, the displayed status updates to "SNAPSHOT" and a message appears indicating that publishing is complete. If you want the asset to no longer be snapshotted, remove the asset and add it again, making sure to uncheck **Create Snapshot**.

## Create a project {: #create-a-project }

You can create new projects directly from the **AI Catalog**; you can also use listed datasets as a source for [predictions](predict).

To create a project, from the catalog main listing, click on an asset to select it. In the upper right, click **Create project**.

![](images/catalog-10.png)

DataRobot runs [EDA1](eda-explained#eda1) and loads the project. When complete, DataRobot displays the [**Start**](model-data) screen.