## Export enriched assets to CSV

URL: https://docs.atlan.com/product/capabilities/build-apps/sdks/python/packages/how-tos/export-assets

> Export enriched assets to CSV from Atlan using AssetExportBasic and the Python SDK (pyatlan). Extracts assets enriched through the Atlan UI.

# AssetExportBasic: export assets to CSV

Use `AssetExportBasic` in the Atlan Python SDK to programmatically export enriched assets to CSV.

The [asset export (basic) package](https://docs.atlan.com/llms/catalog/discovery/asset-export-enriched/llms.txt) identifies all assets that might have been enriched in some way through Atlan's UI and extracts them. The resulting CSV file can be modified or enriched, and then loaded back using the [asset import package](https://docs.atlan.com/llms/catalog/discovery/asset-import/llms.txt).

## All assets

In this example, you're building and running the `asset-export` workflow to export all assets.

However, you can also use one of the following methods to customize the scope of your asset export workflow:

- `enriched_only()`: sets up the package to export only assets enriched by users.
- `glossaries_only()`: sets up the package to export only glossaries.
- `products_only()`: sets up the package to export only data products.
- `all_assets()`: sets up the package to export all assets, whether enriched by users or not, will be exported.

### Java

:::warning[Coming soon]
:::

### Python

```python showLineNumbers title="Import assets from the object store"
from pyatlan.client.atlan import AtlanClient
from pyatlan.model.packages import AssetExportBasic

client = AtlanClient()

workflow = (
 AssetExportBasic() # (1)
 .all_assets( # (2)
 prefix="default",
 include_description=True,
 include_glossaries=True,
 include_data_products=True,
 include_archived=True,
 )
 .object_store(prefix="/test/prefix") # (3)
 .s3( # (4)
 access_key="test-access-key",
 secret_key="test-secret-key",
 bucket="my-bucket",
 region="us-west-1",
 )
).to_workflow() # (5)

response = client.workflow.run(workflow) # (6)
```

1. The `AssetExportBasic` package exports assets from Atlan.
2. In this example, you're building a workflow to export `all_assets()`.
However, you can also use one of the following methods to
customize the scope of your asset export workflow:

 - `enriched_only()`: sets up the package to export only assets enriched by users.
 - `glossaries_only()`: sets up the package to export only glossaries.
 - `products_only()`: sets up the package to export only data products.
 - `all_assets()`: sets up the package to export all assets, whether
 enriched by users or not, will be exported.

 For `all_assets()`, you need to provide following:

 - `prefix`: starting value for a `qualifiedName` that
 will determine which assets to export, default: `default` (all data assets).
 - `include_description`: whether to extract only user-entered description
 (`False`), or to also include system-level description (`True`).
 - `include_glossaries`: whether glossaries (and their terms
 and categories) should be exported (`True`) or not (`False`).
 - `include_data_products`: whether data products
 (and their domains) should be exported (`True`) or not (`False`).
 - `include_archived`: whether to include archived
 assets in the export (`True`) or only active assets (`False`).

3. To set up the package to export to an object storage location, you need to provide

 - `prefix`: directory (path) within the object store
 where the exported file will be uploaded.

4. In this example, you're exporting assets to an object storage location using `s3()`.
However, you can use different object storage methods such as `gcs()` or `adls()`.
You can also configure different export delivery methods using one of the following methods:

 - `email()`: sets up the package to deliver the export via email.
 - `direct()`: sets up the package to deliver the export via direct download.

 For `s3()`, you need to provide following:

 - `access_key`: AWS access key.
 - `secret_key`: AWS secret key.
 - `bucket`: S3 bucket to upload the export file to.
 - `region`: name of the AWS region.

5. Convert the package into a `Workflow` object.
6. Run the workflow by invoking the `run()` method
on the workflow client, passing the created object.

 :::warning[Workflows run asynchronously]
Remember that workflows run asynchronously.
See the [packages and workflows introduction](https://docs.atlan.com/llms/platform/python/packages/llms.txt)
for details on how to check the status and wait
until the workflow has been completed.
 :::

### Kotlin

:::warning[Coming soon]
:::

### Raw REST API

:::tip[Create the workflow via UI only]
We recommend creating the workflow only via the UI.
To rerun an existing workflow, see the steps below.
:::

## Re-run existing workflow

To re-run an existing asset export basic workflow:

### Java

:::warning[Coming soon]
:::

### Python

```python showLineNumbers title="Re-run existing asset export basic workflow"
from pyatlan.client.atlan import AtlanClient
from pyatlan.model.enums import WorkflowPackage

client = AtlanClient()

existing = client.workflow.find_by_type( # (1)
 prefix=WorkflowPackage.ASSET_EXPORT_BASIC, max_results=5
)

# Determine which asset export basic workflow (n)

# from the list of results you want to re-run.

response = client.workflow.rerun(existing[n]) # (2)
```

1. You can find workflows by their type using the workflow client `find_by_type()`
method and providing the **prefix** for one of the packages.
In this example, you're doing so for the `AssetExportBasic`. (You can also specify
the **maximum number of resulting workflows** you want to retrieve as results.)
2. Once you've found the workflow you want to re-run,
you can simply call the workflow client `rerun()` method.

 - Optionally, you can use `rerun(idempotent=True)` to avoid re-running a workflow that's already in running or in a pending state.
 This will return details of the already running workflow if found, and by default, it's set to `False`.

 :::warning[Workflows run asynchronously]
Remember that workflows run asynchronously. See the [packages and workflows introduction](https://docs.atlan.com/llms/platform/python/packages/llms.txt)
for details on how you can check the status and wait until the workflow has been completed.
 :::

### Kotlin

:::warning[Coming soon]
:::

### Raw REST API

:::warning[Requires multiple steps through the raw REST API]
1. Find the existing workflow.
2. Send through the resulting re-run request.
:::
```json showLineNumbers title="POST /api/service/workflows/indexsearch"
{
 "from": 0,
 "size": 5,
 "query": {
 "bool": {
 "filter": [
 {
 "nested": {
 "path": "metadata",
 "query": {
 "prefix": {
 "metadata.name.keyword": {
 "value": "csa-asset-export-basic" // (1)
 }
 }
 }
 }
 }
 ]
 }
 },
 "sort": [
 {
 "metadata.creationTimestamp": {
 "nested": {
 "path": "metadata"
 },
 "order": "desc"
 }
 }
 ],
 "track_total_hits": true
}
```

1. Searching by the `csa-asset-export-basic` prefix will make sure you only find existing asset export basic workflows.

 :::tip[Name of the workflow]
The name of the workflow will be nested within the `_source.metadata.name` property of the response object.
(Remember since this is a search, there could be multiple results, so you may want to use the other
details in each result to determine which workflow you really want.)
 :::
```json title="POST /api/service/workflows/submit"
{
 "namespace": "default",
 "resourceKind": "WorkflowTemplate",
 "resourceName": "csa-asset-export-basic-1684500411" // (1)
}
```

2. Send the name of the workflow as the `resourceName` to rerun it.

---